Abstract

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonics devices. We briefly review recent progress of this field of research and show data-driven applications, including spectrum prediction, inverse design and performance optimization, for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency (PIT) effect, which can be theoretically demonstrated by using the transfer matrix method. Some traditional machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, evolutionary algorithms, including single-objective (genetic algorithm) and multi-objective optimization (NSGA-II), are used to achieve the steep transmission characteristics of PIT effect by synthetically taking many different performance metrics into consideration. The maximum difference between the transmission peaks and dips in the optimized transmission spectrum reaches 0.97. In comparison to previous works, we provide a guidance for intelligent design of photonics devices based on machine learning and evolutionary algorithms and a reference for the selection of machine learning algorithms for simple inverse design problems.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
    [Crossref]
  2. W. Bogaerts and L. Chrostowski, “Silicon photonics circuit design: methods, tools and challenges,” Laser Photonics Rev. 12(4), 1700237 (2018).
    [Crossref]
  3. K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8(3), 339–366 (2019).
    [Crossref]
  4. A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
    [Crossref]
  5. A. Roberts, D. Cormode, C. Reynolds, and T. Newhouse-Illige, “Response of graphene to femtosecond high-intensity laser irradiation,” Appl. Phys. Lett. 99(5), 051912 (2011).
    [Crossref]
  6. E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
    [Crossref]
  7. Q. Bao and K. P. Loh, “Graphene photonics, plasmonics, and broadband optoelectronic devices,” ACS Nano 6(5), 3677–3694 (2012).
    [Crossref]
  8. T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
    [Crossref]
  9. R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
    [Crossref]
  10. A. K. Geim and K. S. Novoselov, “The rise of graphene,” Nat. Mater. 6(3), 183–191 (2007).
    [Crossref]
  11. M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
    [Crossref]
  12. T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
    [Crossref]
  13. S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
    [Crossref]
  14. M. Amin, M. Farhat, and H. Bağcı, “An ultra-broadband multilayered graphene absorber,” Opt. Express 21(24), 29938–29948 (2013).
    [Crossref]
  15. X. Han, T. Wang, X. Li, S. Xiao, and Y. Zhu, “Dynamically tunable plasmon induced transparency in a graphene-based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,” Opt. Express 23(25), 31945–31955 (2015).
    [Crossref]
  16. T. Zhang, X. Yin, L. Chen, and X. Li, “Ultra-compact polarization beam splitter utilizing a graphene-based asymmetrical directional coupler,” Opt. Lett. 41(2), 356–359 (2016).
    [Crossref]
  17. H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
    [Crossref]
  18. S.-X. Xia, X. Zhai, L.-L. Wang, and S.-C. Wen, “Plasmonically induced transparency in double-layered graphene nanoribbons,” Photonics Res. 6(7), 692–702 (2018).
    [Crossref]
  19. H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
    [Crossref]
  20. T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
    [Crossref]
  21. J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61, 85–117 (2015).
    [Crossref]
  22. T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).
    [Crossref]
  23. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
    [Crossref]
  24. M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett. 42, 11–24 (2014).
    [Crossref]
  25. M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).
  26. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).
  27. S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in 2017 IEEE international conference on robotics and automation (ICRA), (IEEE, 2017), 3389–3396.
  28. J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
    [Crossref]
  29. S. Inampudi and H. Mosallaei, “Neural network based design of metagratings,” Appl. Phys. Lett. 112(24), 241102 (2018).
    [Crossref]
  30. I. Balin, V. Garmider, Y. Long, and I. Abdulhalim, “Training artificial neural network for optimization of nanostructured VO2-based smart window performance,” Opt. Express 27(16), A1030–A1040 (2019).
    [Crossref]
  31. A. M. Hammond and R. M. Camacho, “Designing integrated photonic devices using artificial neural networks,” Opt. Express 27(21), 29620–29638 (2019).
    [Crossref]
  32. D. Gostimirovic and N. Y. Winnie, “An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers,” IEEE J. Sel. Top. Quantum Electron. 25(3), 1–5 (2019).
    [Crossref]
  33. J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
    [Crossref]
  34. M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
    [Crossref]
  35. S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).
  36. C. C. Nadell, B. Huang, J. M. Malof, and W. J. Padilla, “Deep learning for accelerated all-dielectric metasurface design,” Opt. Express 27(20), 27523–27535 (2019).
    [Crossref]
  37. J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
    [Crossref]
  38. T. Asano and S. Noda, “Optimization of photonic crystal nanocavities based on deep learning,” Opt. Express 26(25), 32704–32717 (2018).
    [Crossref]
  39. T. Asano and S. Noda, “Iterative optimization of photonic crystal nanocavity designs by using deep neural networks,” Nanophotonics 8(12), 2243–2256 (2019).
    [Crossref]
  40. Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
    [Crossref]
  41. X. Li, J. Shu, W. Gu, and L. Gao, “Deep neural network for plasmonic sensor modeling,” Opt. Mater. Express 9(9), 3857–3862 (2019).
    [Crossref]
  42. Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
    [Crossref]
  43. W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
    [Crossref]
  44. L. Gao, X. Li, D. Liu, L. Wang, and Z Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. (2019).
  45. D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
    [Crossref]
  46. Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
    [Crossref]
  47. Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
    [Crossref]
  48. J. Jiang and J. A. Fan, “Simulator-based training of generative neural networks for the inverse design of metasurfaces,” Nanoscale (2019).
  49. W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” arXiv:1901.10819 (2019).
  50. J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
    [Crossref]
  51. Z. Huang, X. Liu, and J. Zang, “The inverse design of structural color using machine learning,” Nanoscale 11(45), 21748–21758 (2019).
    [Crossref]
  52. I. Sajedian, T. Badloe, and J. Rho, “Optimisation of colour generation from dielectric nanostructures using reinforcement learning,” Opt. Express 27(4), 5874–5883 (2019).
    [Crossref]
  53. I. Sajedian, T. Badloe, and J. Rho, “Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks,” Microsyst. Nanoeng. 5(1), 27 (2019).
    [Crossref]
  54. I. Sajedian, T. Badloe, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9(1), 10899–8 (2019).
    [Crossref]
  55. M. Turduev, E. Bor, C. Latifoglu, I. H. Giden, Y. S. Hanay, and H. Kurt, “Ultra-compact photonic structure design for strong light confinement and coupling into nano-waveguide,” J. Lightwave Technol. 36(14), 2812–2819 (2018).
    [Crossref]
  56. A. da Silva Ferreira, C. H. da Silva Santos, M. S. Gonçalves, and H. E. H. Figueroa, “Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices,” Appl. Soft Comput. 65, 1–11 (2018).
    [Crossref]
  57. R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
    [Crossref]
  58. A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
    [Crossref]
  59. D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
    [Crossref]
  60. K. Were, D. T. Bui, Ø.B. Dick, and B. R. Singh, “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape,” Ecol. Indic. 52, 394–403 (2015).
    [Crossref]
  61. A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
    [Crossref]
  62. Z. Lin, X. Liang, M. Lončar, S. G. Johnson, and A. W. Rodriguez, “Cavity-enhanced second-harmonic generation via nonlinear-overlap optimization,” Optica 3(3), 233–238 (2016).
    [Crossref]
  63. T. W. Hughes, M. Minkov, I. A. Williamson, and S. Fan, “Adjoint method and inverse design for nonlinear nanophotonic devices,” ACS Photonics 5(12), 4781–4787 (2018).
    [Crossref]
  64. T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5(7), 864–871 (2018).
    [Crossref]
  65. A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
    [Crossref]
  66. N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363(6433), 1333–1338 (2019).
    [Crossref]
  67. B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 µm2 footprint,” Nat. Photonics 9(6), 378–382 (2015).
    [Crossref]
  68. H. Cui, X. Sun, and Z. Yu, “Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint,” Opt. Lett. 42(16), 3093 (2017).
    [Crossref]
  69. J. C. Mak, C. Sideris, J. Jeong, A. Hajimiri, and J. K. Poon, “Binary particle swarm optimized 2×2 power splitters in a standard foundry silicon photonic platform,” Opt. Lett. 41(16), 3868 (2016).
    [Crossref]
  70. T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
    [Crossref]
  71. Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
    [Crossref]
  72. Y. Xing, D. Spina, A. Li, T. Dhaene, and W. Bogaerts, “Stochastic collocation for device-level variability analysis in integrated photonics,” Photonics Res. 4(2), 93–100 (2016).
    [Crossref]
  73. S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9(4), 1842–1863 (2019).
    [Crossref]
  74. J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
    [Crossref]
  75. A. V. Zayats, I. I. Smolyaninov, and A. A. Maradudin, “Nano-optics of surface plasmon polaritons,” Phys. Rep. 408(3-4), 131–314 (2005).
    [Crossref]
  76. M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared frequencies,” Phys. Rev. B 80(24), 245435 (2009).
    [Crossref]
  77. T. Zhang, L. Chen, and X. Li, “Graphene-based tunable broadband hyperlens for far-field subdiffraction imaging at mid-infrared frequencies,” Opt. Express 21(18), 20888–20899 (2013).
    [Crossref]
  78. A. Vakil and N. Engheta, “Transformation optics using graphene,” Science 332(6035), 1291–1294 (2011).
    [Crossref]
  79. L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
    [Crossref]
  80. M. A. Othman, C. Guclu, and F. Capolino, “Graphene-based tunable hyperbolic metamaterials and enhanced near-field absorption,” Opt. Express 21(6), 7614–7632 (2013).
    [Crossref]
  81. S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
    [Crossref]
  82. R. Alaee, M. Farhat, C. Rockstuhl, and F. Lederer, “A perfect absorber made of a graphene micro-ribbon metamaterial,” Opt. Express 20(27), 28017–28024 (2012).
    [Crossref]
  83. D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
    [Crossref]
  84. T. Zhang, L. Chen, B. Wang, and X. Li, “Tunable broadband plasmonic field enhancement on a graphene surface using a normal-incidence plane wave at mid-infrared frequencies,” Sci. Rep. 5(1), 11195 (2015).
    [Crossref]
  85. H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
    [Crossref]
  86. A. Y. Nikitin, F. Guinea, F. J. García-Vidal, and L. Martín-Moreno, “Edge and waveguide terahertz surface plasmon modes in graphene microribbons,” Phys. Rev. B 84(16), 161407 (2011).
    [Crossref]
  87. T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
    [Crossref]
  88. R. D. Kekatpure, E. S. Barnard, W. Cai, and M. L. Brongersma, “Phase-coupled plasmon-induced transparency,” Phys. Rev. Lett. 104(24), 243902 (2010).
    [Crossref]
  89. A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
    [Crossref]
  90. L. F. Frellsen, Y. Ding, O. Sigmund, and L. H. Frandsen, “Topology optimized mode multiplexing in silicon-on-insulator photonic wire waveguides,” Opt. Express 24(15), 16866–16873 (2016).
    [Crossref]
  91. L. Du, D. Tang, and X. Yuan, “Edge-reflection phase directed plasmonic resonances on graphene nano-structures,” Opt. Express 22(19), 22689–22698 (2014).
    [Crossref]
  92. C. Zeng, J. Guo, and X. Liu, “High-contrast electro-optic modulation of spatial light induced by graphene-integrated Fabry-Pérot microcavity,” Appl. Phys. Lett. 105(12), 121103 (2014).
    [Crossref]
  93. M. Maltamo and A. Kangas, “Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution,” Can. J. For. Res. 28(8), 1107–1115 (1998).
    [Crossref]
  94. A. Swetapadma and A. Yadav, “A novel decision tree regression-based fault distance estimation scheme for transmission lines,” IEEE Trans. Power Delivery 32(1), 234–245 (2017).
    [Crossref]
  95. A. Liaw and M. Wiener, “Classification and regression by randomForest,” R news 2, 18–22 (2002).
  96. P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn 63(1), 3–42 (2006).
    [Crossref]
  97. C. M. Bishop, Pattern recognition and machine learning (springer, 2006).
  98. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).
  99. A. Chipperfield and P. Fleming, “The MATLAB genetic algorithm toolbox,” From IEE Colloquium on Applied Control Techniques Using MATLAB Digest No. 1995/014 (1995).
  100. K.-H. Han and J.-H. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), (IEEE, 2000), 1354–1360.
  101. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Computat. 6(2), 182–197 (2002).
    [Crossref]
  102. Y. Zhang, D. Liu, X. Shen, J. Bai, Q. Liu, Z. Cheng, P. Tang, and L. Yang, “Design of iodine absorption cell for high-spectral-resolution lidar,” Opt. Express 25(14), 15913–15926 (2017).
    [Crossref]

2020 (1)

R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
[Crossref]

2019 (26)

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
[Crossref]

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

Z. Huang, X. Liu, and J. Zang, “The inverse design of structural color using machine learning,” Nanoscale 11(45), 21748–21758 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Optimisation of colour generation from dielectric nanostructures using reinforcement learning,” Opt. Express 27(4), 5874–5883 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks,” Microsyst. Nanoeng. 5(1), 27 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9(1), 10899–8 (2019).
[Crossref]

N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363(6433), 1333–1338 (2019).
[Crossref]

T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
[Crossref]

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9(4), 1842–1863 (2019).
[Crossref]

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8(3), 339–366 (2019).
[Crossref]

I. Balin, V. Garmider, Y. Long, and I. Abdulhalim, “Training artificial neural network for optimization of nanostructured VO2-based smart window performance,” Opt. Express 27(16), A1030–A1040 (2019).
[Crossref]

A. M. Hammond and R. M. Camacho, “Designing integrated photonic devices using artificial neural networks,” Opt. Express 27(21), 29620–29638 (2019).
[Crossref]

D. Gostimirovic and N. Y. Winnie, “An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers,” IEEE J. Sel. Top. Quantum Electron. 25(3), 1–5 (2019).
[Crossref]

J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
[Crossref]

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

C. C. Nadell, B. Huang, J. M. Malof, and W. J. Padilla, “Deep learning for accelerated all-dielectric metasurface design,” Opt. Express 27(20), 27523–27535 (2019).
[Crossref]

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Asano and S. Noda, “Iterative optimization of photonic crystal nanocavity designs by using deep neural networks,” Nanophotonics 8(12), 2243–2256 (2019).
[Crossref]

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

X. Li, J. Shu, W. Gu, and L. Gao, “Deep neural network for plasmonic sensor modeling,” Opt. Mater. Express 9(9), 3857–3862 (2019).
[Crossref]

Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
[Crossref]

2018 (16)

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).
[Crossref]

T. Asano and S. Noda, “Optimization of photonic crystal nanocavities based on deep learning,” Opt. Express 26(25), 32704–32717 (2018).
[Crossref]

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

W. Bogaerts and L. Chrostowski, “Silicon photonics circuit design: methods, tools and challenges,” Laser Photonics Rev. 12(4), 1700237 (2018).
[Crossref]

S.-X. Xia, X. Zhai, L.-L. Wang, and S.-C. Wen, “Plasmonically induced transparency in double-layered graphene nanoribbons,” Photonics Res. 6(7), 692–702 (2018).
[Crossref]

M. Turduev, E. Bor, C. Latifoglu, I. H. Giden, Y. S. Hanay, and H. Kurt, “Ultra-compact photonic structure design for strong light confinement and coupling into nano-waveguide,” J. Lightwave Technol. 36(14), 2812–2819 (2018).
[Crossref]

A. da Silva Ferreira, C. H. da Silva Santos, M. S. Gonçalves, and H. E. H. Figueroa, “Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices,” Appl. Soft Comput. 65, 1–11 (2018).
[Crossref]

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

S. Inampudi and H. Mosallaei, “Neural network based design of metagratings,” Appl. Phys. Lett. 112(24), 241102 (2018).
[Crossref]

T. W. Hughes, M. Minkov, I. A. Williamson, and S. Fan, “Adjoint method and inverse design for nonlinear nanophotonic devices,” ACS Photonics 5(12), 4781–4787 (2018).
[Crossref]

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5(7), 864–871 (2018).
[Crossref]

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

2017 (5)

A. Swetapadma and A. Yadav, “A novel decision tree regression-based fault distance estimation scheme for transmission lines,” IEEE Trans. Power Delivery 32(1), 234–245 (2017).
[Crossref]

Y. Zhang, D. Liu, X. Shen, J. Bai, Q. Liu, Z. Cheng, P. Tang, and L. Yang, “Design of iodine absorption cell for high-spectral-resolution lidar,” Opt. Express 25(14), 15913–15926 (2017).
[Crossref]

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

H. Cui, X. Sun, and Z. Yu, “Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint,” Opt. Lett. 42(16), 3093 (2017).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

2016 (5)

2015 (8)

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

K. Were, D. T. Bui, Ø.B. Dick, and B. R. Singh, “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape,” Ecol. Indic. 52, 394–403 (2015).
[Crossref]

B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 µm2 footprint,” Nat. Photonics 9(6), 378–382 (2015).
[Crossref]

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

T. Zhang, L. Chen, B. Wang, and X. Li, “Tunable broadband plasmonic field enhancement on a graphene surface using a normal-incidence plane wave at mid-infrared frequencies,” Sci. Rep. 5(1), 11195 (2015).
[Crossref]

X. Han, T. Wang, X. Li, S. Xiao, and Y. Zhu, “Dynamically tunable plasmon induced transparency in a graphene-based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,” Opt. Express 23(25), 31945–31955 (2015).
[Crossref]

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61, 85–117 (2015).
[Crossref]

2014 (5)

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett. 42, 11–24 (2014).
[Crossref]

L. Du, D. Tang, and X. Yuan, “Edge-reflection phase directed plasmonic resonances on graphene nano-structures,” Opt. Express 22(19), 22689–22698 (2014).
[Crossref]

C. Zeng, J. Guo, and X. Liu, “High-contrast electro-optic modulation of spatial light induced by graphene-integrated Fabry-Pérot microcavity,” Appl. Phys. Lett. 105(12), 121103 (2014).
[Crossref]

2013 (6)

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

T. Zhang, L. Chen, and X. Li, “Graphene-based tunable broadband hyperlens for far-field subdiffraction imaging at mid-infrared frequencies,” Opt. Express 21(18), 20888–20899 (2013).
[Crossref]

M. A. Othman, C. Guclu, and F. Capolino, “Graphene-based tunable hyperbolic metamaterials and enhanced near-field absorption,” Opt. Express 21(6), 7614–7632 (2013).
[Crossref]

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

M. Amin, M. Farhat, and H. Bağcı, “An ultra-broadband multilayered graphene absorber,” Opt. Express 21(24), 29938–29948 (2013).
[Crossref]

H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
[Crossref]

2012 (3)

Q. Bao and K. P. Loh, “Graphene photonics, plasmonics, and broadband optoelectronic devices,” ACS Nano 6(5), 3677–3694 (2012).
[Crossref]

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

R. Alaee, M. Farhat, C. Rockstuhl, and F. Lederer, “A perfect absorber made of a graphene micro-ribbon metamaterial,” Opt. Express 20(27), 28017–28024 (2012).
[Crossref]

2011 (6)

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

A. Roberts, D. Cormode, C. Reynolds, and T. Newhouse-Illige, “Response of graphene to femtosecond high-intensity laser irradiation,” Appl. Phys. Lett. 99(5), 051912 (2011).
[Crossref]

A. Vakil and N. Engheta, “Transformation optics using graphene,” Science 332(6035), 1291–1294 (2011).
[Crossref]

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

A. Y. Nikitin, F. Guinea, F. J. García-Vidal, and L. Martín-Moreno, “Edge and waveguide terahertz surface plasmon modes in graphene microribbons,” Phys. Rev. B 84(16), 161407 (2011).
[Crossref]

2010 (2)

E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
[Crossref]

R. D. Kekatpure, E. S. Barnard, W. Cai, and M. L. Brongersma, “Phase-coupled plasmon-induced transparency,” Phys. Rev. Lett. 104(24), 243902 (2010).
[Crossref]

2009 (1)

M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared frequencies,” Phys. Rev. B 80(24), 245435 (2009).
[Crossref]

2008 (2)

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

2007 (1)

A. K. Geim and K. S. Novoselov, “The rise of graphene,” Nat. Mater. 6(3), 183–191 (2007).
[Crossref]

2006 (1)

P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn 63(1), 3–42 (2006).
[Crossref]

2005 (1)

A. V. Zayats, I. I. Smolyaninov, and A. A. Maradudin, “Nano-optics of surface plasmon polaritons,” Phys. Rep. 408(3-4), 131–314 (2005).
[Crossref]

2002 (2)

A. Liaw and M. Wiener, “Classification and regression by randomForest,” R news 2, 18–22 (2002).

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Computat. 6(2), 182–197 (2002).
[Crossref]

1998 (1)

M. Maltamo and A. Kangas, “Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution,” Can. J. For. Res. 28(8), 1107–1115 (1998).
[Crossref]

Abdulhalim, I.

Abdullah, H.

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

Abdullah, M.

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

Agarwal, A. M.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Agarwal, S.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Computat. 6(2), 182–197 (2002).
[Crossref]

Ahmad, A.

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

Ahn, J.-H.

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

Alaee, R.

Altug, H.

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Amin, M.

An, S.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Antonoglou, I.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

Asano, T.

T. Asano and S. Noda, “Iterative optimization of photonic crystal nanocavity designs by using deep neural networks,” Nanophotonics 8(12), 2243–2256 (2019).
[Crossref]

T. Asano and S. Noda, “Optimization of photonic crystal nanocavities based on deep learning,” Opt. Express 26(25), 32704–32717 (2018).
[Crossref]

Babinec, T. M.

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

Badloe, T.

I. Sajedian, T. Badloe, and J. Rho, “Optimisation of colour generation from dielectric nanostructures using reinforcement learning,” Opt. Express 27(4), 5874–5883 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9(1), 10899–8 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks,” Microsyst. Nanoeng. 5(1), 27 (2019).
[Crossref]

Bae, S.-H.

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

Bagci, H.

Bai, J.

Balandin, A. A.

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

Balin, I.

Bao, Q.

Q. Bao and K. P. Loh, “Graphene photonics, plasmonics, and broadband optoelectronic devices,” ACS Nano 6(5), 3677–3694 (2012).
[Crossref]

Bao, W.

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

Barnard, E. S.

R. D. Kekatpure, E. S. Barnard, W. Cai, and M. L. Brongersma, “Phase-coupled plasmon-induced transparency,” Phys. Rev. Lett. 104(24), 243902 (2010).
[Crossref]

Baxter, J.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Bechtel, H. A.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Berini, P.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Bishop, C. M.

C. M. Bishop, Pattern recognition and machine learning (springer, 2006).

Blake, P.

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

Blondel, M.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Bogaerts, W.

W. Bogaerts and L. Chrostowski, “Silicon photonics circuit design: methods, tools and challenges,” Laser Photonics Rev. 12(4), 1700237 (2018).
[Crossref]

Y. Xing, D. Spina, A. Li, T. Dhaene, and W. Bogaerts, “Stochastic collocation for device-level variability analysis in integrated photonics,” Photonics Res. 4(2), 93–100 (2016).
[Crossref]

Bojarski, M.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Booth, T. J.

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

Bor, E.

Brongersma, M. L.

R. D. Kekatpure, E. S. Barnard, W. Cai, and M. L. Brongersma, “Phase-coupled plasmon-induced transparency,” Phys. Rev. Lett. 104(24), 243902 (2010).
[Crossref]

Brucher, M.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Bui, D. T.

K. Were, D. T. Bui, Ø.B. Dick, and B. R. Singh, “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape,” Ecol. Indic. 52, 394–403 (2015).
[Crossref]

Buljan, H.

M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared frequencies,” Phys. Rev. B 80(24), 245435 (2009).
[Crossref]

Cai, W.

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

R. D. Kekatpure, E. S. Barnard, W. Cai, and M. L. Brongersma, “Phase-coupled plasmon-induced transparency,” Phys. Rev. Lett. 104(24), 243902 (2010).
[Crossref]

Calizo, I.

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

Camacho, R. M.

Cambria, E.

T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).
[Crossref]

Campbell, S. D.

S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9(4), 1842–1863 (2019).
[Crossref]

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

Canorenteria, F.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Capolino, F.

Cheben, P.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Chen, L.

Chen, S.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Chen, Y.

Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
[Crossref]

Cheng, F.

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” arXiv:1901.10819 (2019).

Cheng, Z.

Chi, C.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Chipperfield, A.

A. Chipperfield and P. Fleming, “The MATLAB genetic algorithm toolbox,” From IEE Colloquium on Applied Control Techniques Using MATLAB Digest No. 1995/014 (1995).

Chrostowski, L.

W. Bogaerts and L. Chrostowski, “Silicon photonics circuit design: methods, tools and challenges,” Laser Photonics Rev. 12(4), 1700237 (2018).
[Crossref]

Cormode, D.

A. Roberts, D. Cormode, C. Reynolds, and T. Newhouse-Illige, “Response of graphene to femtosecond high-intensity laser irradiation,” Appl. Phys. Lett. 99(5), 051912 (2011).
[Crossref]

Cournapeau, D.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Cui, H.

da Silva Ferreira, A.

A. da Silva Ferreira, C. H. da Silva Santos, M. S. Gonçalves, and H. E. H. Figueroa, “Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices,” Appl. Soft Comput. 65, 1–11 (2018).
[Crossref]

da Silva Santos, C. H.

A. da Silva Ferreira, C. H. da Silva Santos, M. S. Gonçalves, and H. E. H. Figueroa, “Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices,” Appl. Soft Comput. 65, 1–11 (2018).
[Crossref]

Dahl, G. E.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Dai, J.

T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
[Crossref]

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

Dai, Y.

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

Dan, Y.

de Abajo, F. J. G.

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Deb, K.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Computat. 6(2), 182–197 (2002).
[Crossref]

Del Testa, D.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Delacy, B.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Deng, L.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Dezfouli, M. K.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Dhaene, T.

Y. Xing, D. Spina, A. Li, T. Dhaene, and W. Bogaerts, “Stochastic collocation for device-level variability analysis in integrated photonics,” Photonics Res. 4(2), 93–100 (2016).
[Crossref]

Dick, Ø.B.

K. Were, D. T. Bui, Ø.B. Dick, and B. R. Singh, “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape,” Ecol. Indic. 52, 394–403 (2015).
[Crossref]

Ding, J.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Ding, Y.

Du, L.

Dubourg, V.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Duchesnay, E.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Duesberg, G. S.

H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
[Crossref]

Dworakowski, D.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Easum, J. A.

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

Echtermeyer, T. J.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Edwards, B.

N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363(6433), 1333–1338 (2019).
[Crossref]

Eiden, A. L.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Engheta, N.

N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363(6433), 1333–1338 (2019).
[Crossref]

A. Vakil and N. Engheta, “Transformation optics using graphene,” Science 332(6035), 1291–1294 (2011).
[Crossref]

Ernst, D.

P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn 63(1), 3–42 (2006).
[Crossref]

Estakhri, N. M.

N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363(6433), 1333–1338 (2019).
[Crossref]

Etezadi, D.

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Fan, J. A.

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9(4), 1842–1863 (2019).
[Crossref]

J. Jiang and J. A. Fan, “Simulator-based training of generative neural networks for the inverse design of metasurfaces,” Nanoscale (2019).

Fan, S.

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5(7), 864–871 (2018).
[Crossref]

T. W. Hughes, M. Minkov, I. A. Williamson, and S. Fan, “Adjoint method and inverse design for nonlinear nanophotonic devices,” ACS Photonics 5(12), 4781–4787 (2018).
[Crossref]

Fan, Y.

Fang, Z.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Farhat, M.

Fengb, N.

Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
[Crossref]

Ferrari, A. C.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Figueroa, H. E. H.

A. da Silva Ferreira, C. H. da Silva Santos, M. S. Gonçalves, and H. E. H. Figueroa, “Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices,” Appl. Soft Comput. 65, 1–11 (2018).
[Crossref]

Firner, B.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Fleming, P.

A. Chipperfield and P. Fleming, “The MATLAB genetic algorithm toolbox,” From IEE Colloquium on Applied Control Techniques Using MATLAB Digest No. 1995/014 (1995).

Flepp, B.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Fowler, C.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Frandsen, L. H.

Frellsen, L. F.

Gao, L.

X. Li, J. Shu, W. Gu, and L. Gao, “Deep neural network for plasmonic sensor modeling,” Opt. Mater. Express 9(9), 3857–3862 (2019).
[Crossref]

L. Gao, X. Li, D. Liu, L. Wang, and Z Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. (2019).

García-Vidal, F. J.

A. Y. Nikitin, F. Guinea, F. J. García-Vidal, and L. Martín-Moreno, “Edge and waveguide terahertz surface plasmon modes in graphene microribbons,” Phys. Rev. B 84(16), 161407 (2011).
[Crossref]

Garmider, V.

Gei, A. K.

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

Geim, A. K.

A. K. Geim and K. S. Novoselov, “The rise of graphene,” Nat. Mater. 6(3), 183–191 (2007).
[Crossref]

Geng, B.

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Geurts, P.

P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn 63(1), 3–42 (2006).
[Crossref]

Ghosh, S.

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

Giden, I. H.

Girit, C.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Gonçalves, M. S.

A. da Silva Ferreira, C. H. da Silva Santos, M. S. Gonçalves, and H. E. H. Figueroa, “Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices,” Appl. Soft Comput. 65, 1–11 (2018).
[Crossref]

Gorbachev, R. V.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Gostimirovic, D.

D. Gostimirovic and N. Y. Winnie, “An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers,” IEEE J. Sel. Top. Quantum Electron. 25(3), 1–5 (2019).
[Crossref]

Goyal, P.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Gramfort, A.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Graves, A.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

Grigorenko, A. N.

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

Grinberg, Y.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Grisel, O.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Gu, S.

S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in 2017 IEEE international conference on robotics and automation (ICRA), (IEEE, 2017), 3389–3396.

Gu, T.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Gu, W.

Guay, J. M.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Guclu, C.

Guinea, F.

A. Y. Nikitin, F. Guinea, F. J. García-Vidal, and L. Martín-Moreno, “Edge and waveguide terahertz surface plasmon modes in graphene microribbons,” Phys. Rev. B 84(16), 161407 (2011).
[Crossref]

Guo, J.

C. Zeng, J. Guo, and X. Liu, “High-contrast electro-optic modulation of spatial light induced by graphene-integrated Fabry-Pérot microcavity,” Appl. Phys. Lett. 105(12), 121103 (2014).
[Crossref]

Hajimiri, A.

Hale, P. J.

E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
[Crossref]

Hammond, A. M.

Han, K.-H.

K.-H. Han and J.-H. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), (IEEE, 2000), 1354–1360.

Han, T.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Han, X.

T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
[Crossref]

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

X. Han, T. Wang, X. Li, S. Xiao, and Y. Zhu, “Dynamically tunable plasmon induced transparency in a graphene-based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,” Opt. Express 23(25), 31945–31955 (2015).
[Crossref]

Hanay, Y. S.

Hao, Z.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Hassan, M.

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

Hazarika, D.

T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).
[Crossref]

He, C.

J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
[Crossref]

He, J.

J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
[Crossref]

He, Y.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Hegde, R. S.

R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
[Crossref]

Hendry, E.

E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
[Crossref]

Hickey, J.

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

Hinton, G.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Holly, E.

S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in 2017 IEEE international conference on robotics and automation (ICRA), (IEEE, 2017), 3389–3396.

Horng, J.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Hoyer, S.

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

Hu, J.

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Huang, B.

Huang, Z.

Z. Huang, X. Liu, and J. Zang, “The inverse design of structural color using machine learning,” Nanoscale 11(45), 21748–21758 (2019).
[Crossref]

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

Hughes, T. W.

T. W. Hughes, M. Minkov, I. A. Williamson, and S. Fan, “Adjoint method and inverse design for nonlinear nanophotonic devices,” ACS Photonics 5(12), 4781–4787 (2018).
[Crossref]

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5(7), 864–871 (2018).
[Crossref]

Hussin, F.

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

iggott, A. Y. P.

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

Inampudi, S.

S. Inampudi and H. Mosallaei, “Neural network based design of metagratings,” Appl. Phys. Lett. 112(24), 241102 (2018).
[Crossref]

Jablan, M.

M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared frequencies,” Phys. Rev. B 80(24), 245435 (2009).
[Crossref]

Jackel, L. D.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Jaitly, N.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Janner, D.

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Janz, S.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Jenkins, R. P.

S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9(4), 1842–1863 (2019).
[Crossref]

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

Jeong, J.

Jha, D.

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

Ji, C.

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

Jiang, J.

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

J. Jiang and J. A. Fan, “Simulator-based training of generative neural networks for the inverse design of metasurfaces,” Nanoscale (2019).

Jiang, M.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Jin, W.

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

Jin, Z.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Jing, L.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Joannopoulos, J. D.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Johnson, S. G.

Ju, L.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

Ju, S.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Kangas, A.

M. Maltamo and A. Kangas, “Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution,” Can. J. For. Res. 28(8), 1107–1115 (1998).
[Crossref]

Karlsson, L.

M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett. 42, 11–24 (2014).
[Crossref]

Kashiwagi, M.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Kavukcuoglu, K.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

Kekatpure, R. D.

R. D. Kekatpure, E. S. Barnard, W. Cai, and M. L. Brongersma, “Phase-coupled plasmon-induced transparency,” Phys. Rev. Lett. 104(24), 243902 (2010).
[Crossref]

Khoram, E.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Kim, H.-Y.

H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
[Crossref]

Kim, J.-H.

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

K.-H. Han and J.-H. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), (IEEE, 2000), 1354–1360.

Kingsbury, B.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Koike-Akino, T.

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

Kojima, K.

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

Kurt, H.

Lagoudakis, K. G.

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

Lai, L.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Längkvist, M.

M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett. 42, 11–24 (2014).
[Crossref]

Lanqiu, Y.

Latifoglu, C.

Lau, C. N.

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

Lederer, F.

Lee, H.-J.

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

Lee, K.

H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
[Crossref]

Lee, K.-T.

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Lee, Y.

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

Lesina, A. C.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Levine, S.

S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in 2017 IEEE international conference on robotics and automation (ICRA), (IEEE, 2017), 3389–3396.

Li, A.

Y. Xing, D. Spina, A. Li, T. Dhaene, and W. Bogaerts, “Stochastic collocation for device-level variability analysis in integrated photonics,” Photonics Res. 4(2), 93–100 (2016).
[Crossref]

Li, B.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Li, H.

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Li, J.

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

Li, X.

Li, Y.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Li, Z.

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

Liang, X.

Z. Lin, X. Liang, M. Lončar, S. G. Johnson, and A. W. Rodriguez, “Cavity-enhanced second-harmonic generation via nonlinear-overlap optimization,” Optica 3(3), 233–238 (2016).
[Crossref]

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Liaw, A.

A. Liaw and M. Wiener, “Classification and regression by randomForest,” R news 2, 18–22 (2002).

Lidorikis, E.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Lillicrap, T.

S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in 2017 IEEE international conference on robotics and automation (ICRA), (IEEE, 2017), 3389–3396.

Limaj, O.

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Lin, C.

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

Lin, F.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Lin, Z.

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

Z. Lin, X. Liang, M. Lončar, S. G. Johnson, and A. W. Rodriguez, “Cavity-enhanced second-harmonic generation via nonlinear-overlap optimization,” Optica 3(3), 233–238 (2016).
[Crossref]

Liu, D.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Y. Zhang, D. Liu, X. Shen, J. Bai, Q. Liu, Z. Cheng, P. Tang, and L. Yang, “Design of iodine absorption cell for high-spectral-resolution lidar,” Opt. Express 25(14), 15913–15926 (2017).
[Crossref]

L. Gao, X. Li, D. Liu, L. Wang, and Z Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. (2019).

Liu, J.

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

Liu, M.

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

Liu, Q.

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
[Crossref]

Y. Zhang, D. Liu, X. Shen, J. Bai, Q. Liu, Z. Cheng, P. Tang, and L. Yang, “Design of iodine absorption cell for high-spectral-resolution lidar,” Opt. Express 25(14), 15913–15926 (2017).
[Crossref]

Liu, T.

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

Liu, X.

Z. Huang, X. Liu, and J. Zang, “The inverse design of structural color using machine learning,” Nanoscale 11(45), 21748–21758 (2019).
[Crossref]

C. Zeng, J. Guo, and X. Liu, “High-contrast electro-optic modulation of spatial light induced by graphene-integrated Fabry-Pérot microcavity,” Appl. Phys. Lett. 105(12), 121103 (2014).
[Crossref]

Liu, Y.

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” arXiv:1901.10819 (2019).

Liu, Z.

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Loh, K. P.

Q. Bao and K. P. Loh, “Graphene photonics, plasmonics, and broadband optoelectronic devices,” ACS Nano 6(5), 3677–3694 (2012).
[Crossref]

Loncar, M.

Long, Y.

Loutfi, A.

M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett. 42, 11–24 (2014).
[Crossref]

Lu, J.

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

Luk’yanchuk, B.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Ma, W.

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” arXiv:1901.10819 (2019).

Mak, J. C.

Malof, J. M.

Maltamo, M.

M. Maltamo and A. Kangas, “Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution,” Can. J. For. Res. 28(8), 1107–1115 (1998).
[Crossref]

Maradudin, A. A.

A. V. Zayats, I. I. Smolyaninov, and A. A. Maradudin, “Nano-optics of surface plasmon polaritons,” Phys. Rep. 408(3-4), 131–314 (2005).
[Crossref]

Martin, M.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Martín-Moreno, L.

A. Y. Nikitin, F. Guinea, F. J. García-Vidal, and L. Martín-Moreno, “Edge and waveguide terahertz surface plasmon modes in graphene microribbons,” Phys. Rev. B 84(16), 161407 (2011).
[Crossref]

McEvoy, N.

H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
[Crossref]

Mei, S.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Melati, D.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Menon, R.

B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 µm2 footprint,” Nat. Photonics 9(6), 378–382 (2015).
[Crossref]

Meyarivan, T.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Computat. 6(2), 182–197 (2002).
[Crossref]

Miao, F.

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

Michel, V.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Mikhailov, S.

E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
[Crossref]

Milana, S.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Minkov, M.

T. W. Hughes, M. Minkov, I. A. Williamson, and S. Fan, “Adjoint method and inverse design for nonlinear nanophotonic devices,” ACS Photonics 5(12), 4781–4787 (2018).
[Crossref]

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5(7), 864–871 (2018).
[Crossref]

Mnih, V.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

Moger, J.

E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
[Crossref]

Mohamed, A.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Molesky, S.

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

Monfort, M.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Mosallaei, H.

S. Inampudi and H. Mosallaei, “Neural network based design of metagratings,” Appl. Phys. Lett. 112(24), 241102 (2018).
[Crossref]

Muller, U.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Nadell, C. C.

Nagao, T.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Nagar, J.

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

Nair, R. R.

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

Nene, P.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Newhouse-Illige, T.

A. Roberts, D. Cormode, C. Reynolds, and T. Newhouse-Illige, “Response of graphene to femtosecond high-intensity laser irradiation,” Appl. Phys. Lett. 99(5), 051912 (2011).
[Crossref]

Nguyen, P.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Nikitin, A. Y.

A. Y. Nikitin, F. Guinea, F. J. García-Vidal, and L. Martín-Moreno, “Edge and waveguide terahertz surface plasmon modes in graphene microribbons,” Phys. Rev. B 84(16), 161407 (2011).
[Crossref]

Noda, S.

T. Asano and S. Noda, “Iterative optimization of photonic crystal nanocavity designs by using deep neural networks,” Nanophotonics 8(12), 2243–2256 (2019).
[Crossref]

T. Asano and S. Noda, “Optimization of photonic crystal nanocavities based on deep learning,” Opt. Express 26(25), 32704–32717 (2018).
[Crossref]

Novoselov, K. S.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

A. K. Geim and K. S. Novoselov, “The rise of graphene,” Nat. Mater. 6(3), 183–191 (2007).
[Crossref]

Okada, H.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Othman, M. A.

Padilla, W. J.

Parsons, K.

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

Passos, A.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Pedregosa, F.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Peres, N. M. R.

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

Perrot, M.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Petykiewicz, J.

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

Peurifoy, J.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Piggott, A. Y.

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

Polson, R.

B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 µm2 footprint,” Nat. Photonics 9(6), 378–382 (2015).
[Crossref]

Poon, J. K.

Poria, S.

T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).
[Crossref]

Pratap, A.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Computat. 6(2), 182–197 (2002).
[Crossref]

Prettenhofer, P.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Pruneri, V.

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Qin, M.

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

Qiu, C.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Qiu, M.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
[Crossref]

Qu, Y.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
[Crossref]

Rahman, H.

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

Ramunno, L.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Ren, Q.

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

Ren, Y.

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

Reynolds, C.

A. Roberts, D. Cormode, C. Reynolds, and T. Newhouse-Illige, “Response of graphene to femtosecond high-intensity laser irradiation,” Appl. Phys. Lett. 99(5), 051912 (2011).
[Crossref]

Rho, J.

I. Sajedian, T. Badloe, and J. Rho, “Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks,” Microsyst. Nanoeng. 5(1), 27 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9(1), 10899–8 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Optimisation of colour generation from dielectric nanostructures using reinforcement learning,” Opt. Express 27(4), 5874–5883 (2019).
[Crossref]

Richardson, K. A.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Riedmiller, M.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

Rivero-Baleine, C.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Roberts, A.

A. Roberts, D. Cormode, C. Reynolds, and T. Newhouse-Illige, “Response of graphene to femtosecond high-intensity laser irradiation,” Appl. Phys. Lett. 99(5), 051912 (2011).
[Crossref]

Rockstuhl, C.

Rodrigo, D.

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Rodrigues, S. P.

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Rodriguez, A. W

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

Rodriguez, A. W.

Saidur, R.

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

Sainath, T. N.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Sajedian, I.

I. Sajedian, T. Badloe, and J. Rho, “Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks,” Microsyst. Nanoeng. 5(1), 27 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9(1), 10899–8 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Optimisation of colour generation from dielectric nanostructures using reinforcement learning,” Opt. Express 27(4), 5874–5883 (2019).
[Crossref]

Sakurai, A.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Sánchez-Postigo, A.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Savchenko, A.

E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
[Crossref]

Schliemann, J.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Schmid, J. H.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Schmidhuber, J.

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61, 85–117 (2015).
[Crossref]

Sell, D.

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9(4), 1842–1863 (2019).
[Crossref]

Senior, A.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Shalaginov, M. Y.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Sharma, B. K.

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

Shen, B.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 µm2 footprint,” Nat. Photonics 9(6), 378–382 (2015).
[Crossref]

Shen, X.

Shen, Y.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Shen, Y. R.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Shi, Y.

Shiomi, J.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Shu, J.

Sideris, C.

Sigmund, O.

Silver, D.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

Simomura, T.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Singh, B. R.

K. Were, D. T. Bui, Ø.B. Dick, and B. R. Singh, “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape,” Ecol. Indic. 52, 394–403 (2015).
[Crossref]

Smolyaninov, I. I.

A. V. Zayats, I. I. Smolyaninov, and A. A. Maradudin, “Nano-optics of surface plasmon polaritons,” Phys. Rep. 408(3-4), 131–314 (2005).
[Crossref]

Soljacic, M.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared frequencies,” Phys. Rev. B 80(24), 245435 (2009).
[Crossref]

Spina, D.

Y. Xing, D. Spina, A. Li, T. Dhaene, and W. Bogaerts, “Stochastic collocation for device-level variability analysis in integrated photonics,” Photonics Res. 4(2), 93–100 (2016).
[Crossref]

Stauber, T.

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

Sun, B.

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

Sun, X.

Sun, Z.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Swetapadma, A.

A. Swetapadma and A. Yadav, “A novel decision tree regression-based fault distance estimation scheme for transmission lines,” IEEE Trans. Power Delivery 32(1), 234–245 (2017).
[Crossref]

Tahersima, M. H.

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

Tan, Y.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Tang, D.

Tang, H.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Tang, P.

Tegmark, M.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Teweldebrhan, D.

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

Thirion, B.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Trushin, M.

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Tsuda, K.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Turduev, M.

Ulin-Avila, E.

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

Unni, R.

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8(3), 339–366 (2019).
[Crossref]

Vakil, A.

A. Vakil and N. Engheta, “Transformation optics using graphene,” Science 332(6035), 1291–1294 (2011).
[Crossref]

Vanderplas, J.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Vanhoucke, V.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Varoquaux, G.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Vuckovic, J.

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

Wang, B.

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

T. Zhang, L. Chen, B. Wang, and X. Li, “Tunable broadband plasmonic field enhancement on a graphene surface using a normal-incidence plane wave at mid-infrared frequencies,” Sci. Rep. 5(1), 11195 (2015).
[Crossref]

Wang, F.

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Wang, J.

T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
[Crossref]

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

Wang, L.

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

L. Gao, X. Li, D. Liu, L. Wang, and Z Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. (2019).

Wang, L.-L.

S.-X. Xia, X. Zhai, L.-L. Wang, and S.-C. Wen, “Plasmonically induced transparency in double-layered graphene nanoribbons,” Photonics Res. 6(7), 692–702 (2018).
[Crossref]

Wang, P.

B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 µm2 footprint,” Nat. Photonics 9(6), 378–382 (2015).
[Crossref]

Wang, Q.

J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
[Crossref]

Wang, T.

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

X. Han, T. Wang, X. Li, S. Xiao, and Y. Zhu, “Dynamically tunable plasmon induced transparency in a graphene-based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,” Opt. Express 23(25), 31945–31955 (2015).
[Crossref]

Weck, A.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Wehenkel, L.

P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn 63(1), 3–42 (2006).
[Crossref]

Weiss, R.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Wen, Q.

W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” arXiv:1901.10819 (2019).

Wen, S.-C.

S.-X. Xia, X. Zhai, L.-L. Wang, and S.-C. Wen, “Plasmonically induced transparency in double-layered graphene nanoribbons,” Photonics Res. 6(7), 692–702 (2018).
[Crossref]

Were, K.

K. Were, D. T. Bui, Ø.B. Dick, and B. R. Singh, “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape,” Ecol. Indic. 52, 394–403 (2015).
[Crossref]

Werner, D. H.

S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9(4), 1842–1863 (2019).
[Crossref]

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

Whiting, E. B.

Wiener, M.

A. Liaw and M. Wiener, “Classification and regression by randomForest,” R news 2, 18–22 (2002).

Wierstra, D.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

Williamson, I. A.

T. W. Hughes, M. Minkov, I. A. Williamson, and S. Fan, “Adjoint method and inverse design for nonlinear nanophotonic devices,” ACS Photonics 5(12), 4781–4787 (2018).
[Crossref]

Winnie, N. Y.

D. Gostimirovic and N. Y. Winnie, “An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers,” IEEE J. Sel. Top. Quantum Electron. 25(3), 1–5 (2019).
[Crossref]

Xia, S.-X.

S.-X. Xia, X. Zhai, L.-L. Wang, and S.-C. Wen, “Plasmonically induced transparency in double-layered graphene nanoribbons,” Photonics Res. 6(7), 692–702 (2018).
[Crossref]

Xiao, S.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

X. Han, T. Wang, X. Li, S. Xiao, and Y. Zhu, “Dynamically tunable plasmon induced transparency in a graphene-based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,” Opt. Express 23(25), 31945–31955 (2015).
[Crossref]

Xie, Y.

Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
[Crossref]

Xing, Y.

Y. Xing, D. Spina, A. Li, T. Dhaene, and W. Bogaerts, “Stochastic collocation for device-level variability analysis in integrated photonics,” Photonics Res. 4(2), 93–100 (2016).
[Crossref]

Xu, C.

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

Xu, D.

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Xu, K.

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

Xu, Y.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” arXiv:1901.10819 (2019).

Yada, K.

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

Yadav, A.

A. Swetapadma and A. Yadav, “A novel decision tree regression-based fault distance estimation scheme for transmission lines,” IEEE Trans. Power Delivery 32(1), 234–245 (2017).
[Crossref]

Yan, X.

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

Yang, J.

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

Yang, J. K. W.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Yang, L.

Yang, Y.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Yao, K.

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8(3), 339–366 (2019).
[Crossref]

Ye, J.

J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
[Crossref]

Yim, C.

H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
[Crossref]

Yin, F.

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

Yin, X.

T. Zhang, X. Yin, L. Chen, and X. Li, “Ultra-compact polarization beam splitter utilizing a graphene-based asymmetrical directional coupler,” Opt. Lett. 41(2), 356–359 (2016).
[Crossref]

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

Young, T.

T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).
[Crossref]

Yu, C.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Yu, D.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Yu, X.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Yu, Z

L. Gao, X. Li, D. Liu, L. Wang, and Z Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. (2019).

Yu, Z.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

H. Cui, X. Sun, and Z. Yu, “Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint,” Opt. Lett. 42(16), 3093 (2017).
[Crossref]

Yuan, X.

Zang, J.

Z. Huang, X. Liu, and J. Zang, “The inverse design of structural color using machine learning,” Nanoscale 11(45), 21748–21758 (2019).
[Crossref]

Zayats, A. V.

A. V. Zayats, I. I. Smolyaninov, and A. A. Maradudin, “Nano-optics of surface plasmon polaritons,” Phys. Rep. 408(3-4), 131–314 (2005).
[Crossref]

Zeng, C.

C. Zeng, J. Guo, and X. Liu, “High-contrast electro-optic modulation of spatial light induced by graphene-integrated Fabry-Pérot microcavity,” Appl. Phys. Lett. 105(12), 121103 (2014).
[Crossref]

Zentgraf, T.

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

Zettl, A.

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Zhai, X.

S.-X. Xia, X. Zhai, L.-L. Wang, and S.-C. Wen, “Plasmonically induced transparency in double-layered graphene nanoribbons,” Photonics Res. 6(7), 692–702 (2018).
[Crossref]

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

Zhang, C.

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

Zhang, H.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Zhang, J.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Zhang, T.

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

T. Zhang, X. Yin, L. Chen, and X. Li, “Ultra-compact polarization beam splitter utilizing a graphene-based asymmetrical directional coupler,” Opt. Lett. 41(2), 356–359 (2016).
[Crossref]

T. Zhang, L. Chen, B. Wang, and X. Li, “Tunable broadband plasmonic field enhancement on a graphene surface using a normal-incidence plane wave at mid-infrared frequencies,” Sci. Rep. 5(1), 11195 (2015).
[Crossref]

T. Zhang, L. Chen, and X. Li, “Graphene-based tunable broadband hyperlens for far-field subdiffraction imaging at mid-infrared frequencies,” Opt. Express 21(18), 20888–20899 (2013).
[Crossref]

Zhang, X.

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Zhang, Y.

Zhao, J.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

Zheng, B.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Zheng, C.

J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
[Crossref]

Zheng, Y.

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8(3), 339–366 (2019).
[Crossref]

Zhou, J.

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

Zhou, L.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Zhou, Y.

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,” J. Lightwave Technol. 35(23), 5142–5149 (2017).
[Crossref]

Zhu, D.

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Zhu, J.

Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
[Crossref]

Zhu, X.

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

Zhu, Y.

Zieba, K.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

ACS Cent. Sci. (1)

A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, “Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,” ACS Cent. Sci. 5(2), 319–326 (2019).
[Crossref]

ACS Nano (4)

Z. Jin, S. Mei, S. Chen, Y. Li, C. Zhang, Y. He, X. Yu, C. Yu, J. K. W. Yang, B. Luk’yanchuk, S. Xiao, and C. Qiu, “Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm,” ACS Nano 13(1), 821–829 (2019).
[Crossref]

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019)..
[Crossref]

Q. Bao and K. P. Loh, “Graphene photonics, plasmonics, and broadband optoelectronic devices,” ACS Nano 6(5), 3677–3694 (2012).
[Crossref]

ACS Photonics (3)

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljacic, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6(5), 1168–1174 (2019).
[Crossref]

T. W. Hughes, M. Minkov, I. A. Williamson, and S. Fan, “Adjoint method and inverse design for nonlinear nanophotonic devices,” ACS Photonics 5(12), 4781–4787 (2018).
[Crossref]

Appl. Phys. Lett. (4)

A. Roberts, D. Cormode, C. Reynolds, and T. Newhouse-Illige, “Response of graphene to femtosecond high-intensity laser irradiation,” Appl. Phys. Lett. 99(5), 051912 (2011).
[Crossref]

H. Li, L. Wang, J. Liu, Z. Huang, B. Sun, and X. Zhai, “Investigation of the graphene based planar plasmonic filters,” Appl. Phys. Lett. 103(21), 211104 (2013).
[Crossref]

C. Zeng, J. Guo, and X. Liu, “High-contrast electro-optic modulation of spatial light induced by graphene-integrated Fabry-Pérot microcavity,” Appl. Phys. Lett. 105(12), 121103 (2014).
[Crossref]

S. Inampudi and H. Mosallaei, “Neural network based design of metagratings,” Appl. Phys. Lett. 112(24), 241102 (2018).
[Crossref]

Appl. Soft Comput. (1)

A. da Silva Ferreira, C. H. da Silva Santos, M. S. Gonçalves, and H. E. H. Figueroa, “Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices,” Appl. Soft Comput. 65, 1–11 (2018).
[Crossref]

Can. J. For. Res. (1)

M. Maltamo and A. Kangas, “Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution,” Can. J. For. Res. 28(8), 1107–1115 (1998).
[Crossref]

Carbon (3)

S.-H. Bae, Y. Lee, B. K. Sharma, H.-J. Lee, J.-H. Kim, and J.-H. Ahn, “Graphene-based transparent strain sensor,” Carbon 51, 236–242 (2013).
[Crossref]

S. Xiao, T. Wang, T. Liu, X. Yan, Z. Li, and C. Xu, “Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,” Carbon 126, 271–278 (2018).
[Crossref]

H. Li, C. Ji, Y. Ren, J. Hu, M. Qin, and L. Wang, “Investigation of multiband plasmonic metamaterial perfect absorbers based on graphene ribbons by the phase-coupled method,” Carbon 141, 481–487 (2019).
[Crossref]

Ecol. Indic. (1)

K. Were, D. T. Bui, Ø.B. Dick, and B. R. Singh, “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape,” Ecol. Indic. 52, 394–403 (2015).
[Crossref]

IEEE Comput. Intell. Mag. (1)

T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).
[Crossref]

IEEE J. Multiscale Multiphys. Comput. Tech. (1)

J. Nagar, S. D. Campbell, Q. Ren, J. A. Easum, R. P. Jenkins, and D. H. Werner, “Multiobjective Optimization-Aided Metamaterials-by-Design With Application to Highly Directive Nanodevices,” IEEE J. Multiscale Multiphys. Comput. Tech. 2, 147–158 (2017).
[Crossref]

IEEE J. Sel. Top. Quantum Electron. (2)

R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
[Crossref]

D. Gostimirovic and N. Y. Winnie, “An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers,” IEEE J. Sel. Top. Quantum Electron. 25(3), 1–5 (2019).
[Crossref]

IEEE Signal Process. Mag. (1)

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

IEEE Trans. Evol. Computat. (1)

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Computat. 6(2), 182–197 (2002).
[Crossref]

IEEE Trans. Power Delivery (1)

A. Swetapadma and A. Yadav, “A novel decision tree regression-based fault distance estimation scheme for transmission lines,” IEEE Trans. Power Delivery 32(1), 234–245 (2017).
[Crossref]

J. Lightwave Technol. (2)

J. Mach. Learn. Res. (1)

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

J. Phys. D: Appl. Phys. (1)

T. Zhang, J. Zhou, J. Dai, Y. Dai, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Plasmon induced absorption in a graphene-based nanoribbon waveguide system and its applications in logic gate and sensor,” J. Phys. D: Appl. Phys. 51(5), 055103 (2018).
[Crossref]

Laser Photonics Rev. (1)

W. Bogaerts and L. Chrostowski, “Silicon photonics circuit design: methods, tools and challenges,” Laser Photonics Rev. 12(4), 1700237 (2018).
[Crossref]

Mach Learn (1)

P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn 63(1), 3–42 (2006).
[Crossref]

Microsyst. Nanoeng. (1)

I. Sajedian, T. Badloe, and J. Rho, “Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks,” Microsyst. Nanoeng. 5(1), 27 (2019).
[Crossref]

Nano Lett. (4)

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

H.-Y. Kim, K. Lee, N. McEvoy, C. Yim, and G. S. Duesberg, “Chemically modulated graphene diodes,” Nano Lett. 13(5), 2182–2188 (2013).
[Crossref]

A. A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, and C. N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Lett. 8(3), 902–907 (2008).
[Crossref]

T. J. Echtermeyer, P. Nene, M. Trushin, R. V. Gorbachev, A. L. Eiden, S. Milana, Z. Sun, J. Schliemann, E. Lidorikis, K. S. Novoselov, and A. C. Ferrari, “Photothermoelectric and photoelectric contributions to light detection in metal–graphene–metal photodetectors,” Nano Lett. 14(7), 3733–3742 (2014).
[Crossref]

Nanophotonics (2)

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8(3), 339–366 (2019).
[Crossref]

T. Asano and S. Noda, “Iterative optimization of photonic crystal nanocavity designs by using deep neural networks,” Nanophotonics 8(12), 2243–2256 (2019).
[Crossref]

Nanoscale (3)

Y. Chen, J. Zhu, Y. Xie, N. Fengb, and Q. Liu, “Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network,” Nanoscale 11(19), 9749–9755 (2019).
[Crossref]

Z. Huang, X. Liu, and J. Zang, “The inverse design of structural color using machine learning,” Nanoscale 11(45), 21748–21758 (2019).
[Crossref]

J. He, C. He, C. Zheng, Q. Wang, and J. Ye, “Plasmonic nanoparticle simulations and inverse design using machine learning,” Nanoscale 11(37), 17444–17459 (2019).
[Crossref]

Nat. Commun. (1)

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).
[Crossref]

Nat. Mater. (1)

A. K. Geim and K. S. Novoselov, “The rise of graphene,” Nat. Mater. 6(3), 183–191 (2007).
[Crossref]

Nat. Nanotechnol. (1)

L. Ju, B. Geng, J. Horng, C. Girit, M. Martin, Z. Hao, H. A. Bechtel, X. Liang, A. Zettl, Y. R. Shen, and F. Wang, “Graphene plasmonics for tunable terahertz metamaterials,” Nat. Nanotechnol. 6(10), 630–634 (2011).
[Crossref]

Nat. Photonics (4)

B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 µm2 footprint,” Nat. Photonics 9(6), 378–382 (2015).
[Crossref]

A. Y. P. iggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9(6), 374–377 (2015).
[Crossref]

S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12(11), 659–670 (2018).
[Crossref]

Nature (1)

M. Liu, X. Yin, E. Ulin-Avila, B. Geng, T. Zentgraf, L. Ju, F. Wang, and X. Zhang, “A graphene-based broadband optical modulator,” Nature 474(7349), 64–67 (2011).
[Crossref]

Neural Netw. (1)

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61, 85–117 (2015).
[Crossref]

Opt. Express (14)

Y. Zhang, D. Liu, X. Shen, J. Bai, Q. Liu, Z. Cheng, P. Tang, and L. Yang, “Design of iodine absorption cell for high-spectral-resolution lidar,” Opt. Express 25(14), 15913–15926 (2017).
[Crossref]

C. C. Nadell, B. Huang, J. M. Malof, and W. J. Padilla, “Deep learning for accelerated all-dielectric metasurface design,” Opt. Express 27(20), 27523–27535 (2019).
[Crossref]

A. M. Hammond and R. M. Camacho, “Designing integrated photonic devices using artificial neural networks,” Opt. Express 27(21), 29620–29638 (2019).
[Crossref]

T. Zhang, J. Wang, Y. Dan, Y. Lanqiu, J. Dai, X. Han, and K. Xu, “Efficient training and design of photonic neural network through neuroevolution,” Opt. Express 27(26), 37150–37163 (2019).
[Crossref]

L. Du, D. Tang, and X. Yuan, “Edge-reflection phase directed plasmonic resonances on graphene nano-structures,” Opt. Express 22(19), 22689–22698 (2014).
[Crossref]

I. Balin, V. Garmider, Y. Long, and I. Abdulhalim, “Training artificial neural network for optimization of nanostructured VO2-based smart window performance,” Opt. Express 27(16), A1030–A1040 (2019).
[Crossref]

T. Asano and S. Noda, “Optimization of photonic crystal nanocavities based on deep learning,” Opt. Express 26(25), 32704–32717 (2018).
[Crossref]

M. Amin, M. Farhat, and H. Bağcı, “An ultra-broadband multilayered graphene absorber,” Opt. Express 21(24), 29938–29948 (2013).
[Crossref]

L. F. Frellsen, Y. Ding, O. Sigmund, and L. H. Frandsen, “Topology optimized mode multiplexing in silicon-on-insulator photonic wire waveguides,” Opt. Express 24(15), 16866–16873 (2016).
[Crossref]

T. Zhang, L. Chen, and X. Li, “Graphene-based tunable broadband hyperlens for far-field subdiffraction imaging at mid-infrared frequencies,” Opt. Express 21(18), 20888–20899 (2013).
[Crossref]

X. Han, T. Wang, X. Li, S. Xiao, and Y. Zhu, “Dynamically tunable plasmon induced transparency in a graphene-based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,” Opt. Express 23(25), 31945–31955 (2015).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Optimisation of colour generation from dielectric nanostructures using reinforcement learning,” Opt. Express 27(4), 5874–5883 (2019).
[Crossref]

R. Alaee, M. Farhat, C. Rockstuhl, and F. Lederer, “A perfect absorber made of a graphene micro-ribbon metamaterial,” Opt. Express 20(27), 28017–28024 (2012).
[Crossref]

M. A. Othman, C. Guclu, and F. Capolino, “Graphene-based tunable hyperbolic metamaterials and enhanced near-field absorption,” Opt. Express 21(6), 7614–7632 (2013).
[Crossref]

Opt. Lett. (3)

Opt. Mater. Express (2)

Optica (2)

Pattern Recognit. Lett. (1)

M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett. 42, 11–24 (2014).
[Crossref]

Photonics Res. (3)

S.-X. Xia, X. Zhai, L.-L. Wang, and S.-C. Wen, “Plasmonically induced transparency in double-layered graphene nanoribbons,” Photonics Res. 6(7), 692–702 (2018).
[Crossref]

Y. Xing, D. Spina, A. Li, T. Dhaene, and W. Bogaerts, “Stochastic collocation for device-level variability analysis in integrated photonics,” Photonics Res. 4(2), 93–100 (2016).
[Crossref]

T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, J. Li, Y. Zhou, and K. Xu, “Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,” Photonics Res. 7(3), 368–380 (2019).
[Crossref]

Phys. Rep. (1)

A. V. Zayats, I. I. Smolyaninov, and A. A. Maradudin, “Nano-optics of surface plasmon polaritons,” Phys. Rep. 408(3-4), 131–314 (2005).
[Crossref]

Phys. Rev. B (2)

M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared frequencies,” Phys. Rev. B 80(24), 245435 (2009).
[Crossref]

A. Y. Nikitin, F. Guinea, F. J. García-Vidal, and L. Martín-Moreno, “Edge and waveguide terahertz surface plasmon modes in graphene microribbons,” Phys. Rev. B 84(16), 161407 (2011).
[Crossref]

Phys. Rev. Lett. (3)

R. D. Kekatpure, E. S. Barnard, W. Cai, and M. L. Brongersma, “Phase-coupled plasmon-induced transparency,” Phys. Rev. Lett. 104(24), 243902 (2010).
[Crossref]

E. Hendry, P. J. Hale, J. Moger, A. Savchenko, and S. Mikhailov, “Coherent nonlinear optical response of graphene,” Phys. Rev. Lett. 105(9), 097401 (2010).
[Crossref]

Y. Li, Y. Xu, M. Jiang, B. Li, T. Han, C. Chi, F. Lin, B. Shen, X. Zhu, L. Lai, and Z. Fang, “Self-Learning Perfect Optical Chirality via a Deep Neural Network,” Phys. Rev. Lett. 123(21), 213902 (2019).
[Crossref]

R news (1)

A. Liaw and M. Wiener, “Classification and regression by randomForest,” R news 2, 18–22 (2002).

Renewable Sustainable Energy Rev. (1)

A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable Sustainable Energy Rev. 33, 102–109 (2014).
[Crossref]

Sci. Adv. (1)

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Sci. Rep. (4)

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep Neural Network Inverse Design of Integrated Photonic Power Splitters,” Sci. Rep. 9(1), 1368 (2019).
[Crossref]

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

I. Sajedian, T. Badloe, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9(1), 10899–8 (2019).
[Crossref]

T. Zhang, L. Chen, B. Wang, and X. Li, “Tunable broadband plasmonic field enhancement on a graphene surface using a normal-incidence plane wave at mid-infrared frequencies,” Sci. Rep. 5(1), 11195 (2015).
[Crossref]

Science (4)

R. R. Nair, P. Blake, A. N. Grigorenko, K. S. Novoselov, T. J. Booth, T. Stauber, N. M. R. Peres, and A. K. Gei, “Fine structure constant defines visual transparency of graphene,” Science 320(5881), 1308 (2008).
[Crossref]

N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363(6433), 1333–1338 (2019).
[Crossref]

A. Vakil and N. Engheta, “Transformation optics using graphene,” Science 332(6035), 1291–1294 (2011).
[Crossref]

D. Rodrigo, O. Limaj, D. Janner, D. Etezadi, F. J. G. de Abajo, V. Pruneri, and H. Altug, “Mid-infrared plasmonic biosensing with graphene,” Science 349(6244), 165–168 (2015).
[Crossref]

Other (10)

L. Gao, X. Li, D. Liu, L. Wang, and Z Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. (2019).

J. Jiang and J. A. Fan, “Simulator-based training of generative neural networks for the inverse design of metasurfaces,” Nanoscale (2019).

W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” arXiv:1901.10819 (2019).

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 (2016).

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 (2013).

S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in 2017 IEEE international conference on robotics and automation (ICRA), (IEEE, 2017), 3389–3396.

C. M. Bishop, Pattern recognition and machine learning (springer, 2006).

A. Chipperfield and P. Fleming, “The MATLAB genetic algorithm toolbox,” From IEE Colloquium on Applied Control Techniques Using MATLAB Digest No. 1995/014 (1995).

K.-H. Han and J.-H. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), (IEEE, 2000), 1354–1360.

S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics (2019).

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (6)

Fig. 1.
Fig. 1. The schematic view of the proposed GMs, which consist of double layer GNRs embedded into insulated dielectric layer with a separation dg=300 nm.
Fig. 2.
Fig. 2. The real part (a) and imaginary part (b) of effective refractive indices for SPPs. The solid lines are the dispersions of SPPs calculated by theoretical model, and the marks are those calculated by the mode solution. (c) Schematic of the single layer grating consisted of GNRs. (d) When mid-infrared wave normally incident on the single layer grating, blue, red and orange lines are the resonance curves for three modes m=1, 3 and 5, respectively. For comparison, the absorption contour patterns of the single layer grating are calculated by the FDTD method. The value of w/Λ is set as 1/4. In (a), (b) and (d), the chemical potentials of graphene μc are set as 0.5 eV.
Fig. 3.
Fig. 3. Transmission spectrums of the proposed GMs based on the FDTD simulation (red solid line) and theoretical model (purple dashed line). The blue dashed line and green dashed line are the transmission spectrums of the GMs that only includes the upper GNRs and the lower GNRs, respectively. The normalized magnetic field distributions of the transmission dips (A (λ=5.30 μm), C (λ=7.04 μm), D (λ=10.40 μm) and F (λ=13.16 μm)) and peaks (B (λ=6.32 μm) and E (λ=12.35 μm)).
Fig. 4.
Fig. 4. The diagram of the forward spectrum prediction. (b) Score and loss for different generations of the GA. (c) Training time and accuracies for different regression algorithms in forward spectrum prediction. (d) The transmission spectrums predicted by the regression algorithms and simulated by the FDTD simulation.
Fig. 5.
Fig. 5. (a) The diagram of the inverse design. (b) The training time and accuracies (scores) for all regression algorithms in inverse design. (c) The structure parameters (chemical potentials μc1, μc2, μc3 and μc4 for the GNRs in the GMs) predicted by all regression algorithms and the ground truth. (d) The FDTD simulated transmission spectrums for the chemical potentials predicted by the regression algorithms.
Fig. 6.
Fig. 6. (a) The fitnesses of the GA, QGA and PSO for different generations in the performance optimization. (b) Optimization results of chemical potentials for the GA, QGA and PSO in the 100th iteration. (c) The optimized transmission spectrums of the GA, QGA and PSO in the first iteration (green line) and the 100th iteration (blue line). (d) The multi-objective optimization results for two differences between one peak (8161 nm) and two dips (7659 nm and 11620 nm). (e) The multi-objective optimization results for four differences between two peaks (6110 nm and 12620 nm) and four dips (5150 nm, 6890 nm, 10310 nm and 13220 nm).

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

σ g = i e 2 k B T π 2 ( ω + i τ 1 ) [ μ c k B T + 2 ln ( exp ( μ c k B T ) + 1 ) ] + i e 2 4 π ln [ 2 | μ c | ( ω + i τ 1 ) 2 | μ c | + ( ω + i τ 1 ) ]
σ f g = i N e 2 μ c π 2 ( ω + i τ 1 )
ε 1 β S P P 2 ε 1 ω 2 c 2 + ε 2 β S P P 2 ε 2 ω 2 c 2 = i σ fg ω ε 0
n S P P = β S P P k 0 = 2 ε 0 ε S i o 2 π 2 c N e 2 μ c ( ω + i τ 1 )
Re ( n S P P ) k 0 w + φ = m π , m = 1 , 2 , 3 ,
ω r  =  ( m φ ) N e 2 μ c 2 ε 0 ε I n t e r 2 w , m = 1 , 2 , 3 ,
H  =  M 2 S 12 M 1
S 12  =  ( e i φ 0 0 e i φ ) , M q = 1 t 21 ( t 12 t 21 r 12 r 21 r 21 r 12 1 ) , q = 1 , 2
{ σ 1  =  i 2 ( r 1 e 2 μ c 1 N ω π 2 ( ω 2 ω r 1 2 ) + i Γ r 1 ω + r 1 e 2 μ c 2 N ω π 2 ( ω 2 ω r 2 2 ) + i Γ r 2 ω ) σ 2  =  i 2 ( r 2 e 2 μ c 3 N ω π 2 ( ω 2 ω r 3 2 ) + i Γ r 3 ω + r 2 e 2 μ c 4 N ω π 2 ( ω 2 ω r 4 2 ) + i Γ r 4 ω )
T = [ 4 n I n t e r 2 ( 2 n I n t e r + Z 0 σ 1 ) ( 2 n I n t e r + Z 0 σ 2 ) e i φ Z 0 2 σ 1 σ 2 e i φ ] 2
F  =  λ min λ max | S 0 ( λ ) S ( λ ) |

Metrics