B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: a review of Bayesian optimization,” Proc. IEEE 104, 148–175 (2016).

[Crossref]

I. Malkiel, A. Nagler, M. Mrejen, U. Arieli, L. Wolf, and H. Suchowski, “Deep learning for design and retrieval of nano-photonic structures,” arXiv:1702.07949 (2017).

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, 374–377 (2015).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” arXiv:1611.02167 (2016).

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

[Crossref]

P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geosci. Remote Sens. Lett. 12, 309–313 (2015).

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]

E. Bor, O. Alparslan, M. Turduev, Y. S. Hanay, H. Kurt, S. I. Arakawa, and M. Murata, “Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter,” Opt. Express 26, 29032–29044 (2018).

[Crossref]

E. Bor, C. Babayigit, H. Kurt, K. Staliunas, and M. Turduev, “Directional invisibility by genetic optimization,” Opt. Lett. 43, 5781–5784 (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, 2812–2819 (2018).

[Crossref]

D. K. Gramotnev and S. I. Bozhevolnyi, “Plasmonics beyond the diffraction limit,” Nat. Photonics 4, 83–91 (2010).

[Crossref]

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

[Crossref]

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

[Crossref]

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “A generative model for inverse design of metamaterials,” arXiv:1805.10181 (2018).

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

[Crossref]

G. Carleo and M. Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science 355, 602–606 (2017).

[Crossref]

G. C. Cawley and N. L. Talbot, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” J. Mach. Learn. Res. 11, 2079–2107 (2010).

Z. Chai, X. Hu, H. Yang, and Q. Gong, “All-optical tunable on-chip plasmon-induced transparency based on two surface-plasmon-polaritons absorption,” Appl. Phys. Lett. 108, 151104 (2016).

[Crossref]

Z. Chai, X. Hu, Y. Zhu, S. Sun, H. Yang, and Q. Gong, “Ultracompact chip-integrated electromagnetically induced transparency in a single plasmonic composite nanocavity,” Adv. Opt. Mater. 2, 320–325 (2014).

[Crossref]

M. Qin, L. Wang, X. Zhai, D. Chen, and S. Xia, “Generating and manipulating high quality factors of Fano resonance in nanoring resonator by stacking a half nanoring,” Nano. Res. Lett. 12, 578 (2017).

[Crossref]

Z. Zhang, L. Zhang, H. Li, and H. Chen, “Plasmon induced transparency in a surface plasmon polariton waveguide with a comb line slot and rectangle cavity,” Appl. Phys. Lett. 104, 231114 (2014).

[Crossref]

Z. Chen and L. Yu, “Multiple Fano resonances based on different waveguide modes in a symmetry breaking plasmonic system,” IEEE Photon. J. 6, 4802208 (2014).

[Crossref]

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

[Crossref]

A. Chipperfield and P. Fleming, “The MATLAB genetic algorithm toolbox,” in IEEE Colloquium on Applied Control Techniques Using MATLAB (1995).

P. B. Johnson and R. Christy, “Optical constants of the noble metals,” Phys. Rev. B 6, 4370–4379 (1972).

[Crossref]

H. Cui, X. Sun, and Z. Yu, “Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint,” Opt. Lett. 42, 3093–3096 (2017).

[Crossref]

Z. Yu, H. Cui, and X. Sun, “Genetically optimized on-chip wideband ultracompact reflectors and Fabry-Perot cavities,” Photon. Res. 5, B15–B19 (2017).

[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]

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]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (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, 5142–5149 (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, 5142–5149 (2017).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (2017).

[Crossref]

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: a review of Bayesian optimization,” Proc. IEEE 104, 148–175 (2016).

[Crossref]

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

[Crossref]

A. Devarakonda, M. Naumov, and M. Garland, “AdaBatch: adaptive batch sizes for training deep neural networks,” arXiv:1712.02029 (2017).

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

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

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (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, 5142–5149 (2017).

[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]

G. Fishman, Monte Carlo: Concepts, Algorithms, and Applications (Springer, 2013).

A. Chipperfield and P. Fleming, “The MATLAB genetic algorithm toolbox,” in IEEE Colloquium on Applied Control Techniques Using MATLAB (1995).

P.-H. Fu, S.-C. Lo, P.-C. Tsai, K.-L. Lee, and P.-K. Wei, “Optimization for gold nanostructure-based surface plasmon biosensors using a microgenetic algorithm,” ACS Photon. 5, 2320–2327 (2018).

[Crossref]

A. Devarakonda, M. Naumov, and M. Garland, “AdaBatch: adaptive batch sizes for training deep neural networks,” arXiv:1712.02029 (2017).

S. Zhang, D. A. Genov, Y. Wang, M. Liu, and X. Zhang, “Plasmon-induced transparency in metamaterials,” Phys. Rev. Lett. 101, 047401 (2008).

[Crossref]

P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geosci. Remote Sens. Lett. 12, 309–313 (2015).

[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. Chai, X. Hu, H. Yang, and Q. Gong, “All-optical tunable on-chip plasmon-induced transparency based on two surface-plasmon-polaritons absorption,” Appl. Phys. Lett. 108, 151104 (2016).

[Crossref]

Z. Chai, X. Hu, Y. Zhu, S. Sun, H. Yang, and Q. Gong, “Ultracompact chip-integrated electromagnetically induced transparency in a single plasmonic composite nanocavity,” Adv. Opt. Mater. 2, 320–325 (2014).

[Crossref]

Y. Zhu, X. Hu, H. Yang, and Q. Gong, “On-chip plasmon-induced transparency based on plasmonic coupled nanocavities,” Sci. Rep. 4, 3752 (2014).

[Crossref]

D. K. Gramotnev and S. I. Bozhevolnyi, “Plasmonics beyond the diffraction limit,” Nat. Photonics 4, 83–91 (2010).

[Crossref]

B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” arXiv:1611.02167 (2016).

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, 5142–5149 (2017).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (2017).

[Crossref]

X. Han, T. Wang, B. Liu, Y. He, and Y. Zhu, “Tunable triple plasmon-induced transparencies in dual T-shaped cavities side-coupled waveguide,” IEEE Photon. Technol. Lett. 28, 347–350 (2016).

[Crossref]

X. Han, T. Wang, X. Li, B. Liu, Y. He, and J. Tang, “Ultrafast and low-power dynamically tunable plasmon-induced transparencies in compact aperture-coupled rectangular resonators,” J. Lightwave Technol. 33, 5133–5139 (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, 31945–31955 (2015).

[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, 2812–2819 (2018).

[Crossref]

E. Bor, O. Alparslan, M. Turduev, Y. S. Hanay, H. Kurt, S. I. Arakawa, and M. Murata, “Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter,” Opt. Express 26, 29032–29044 (2018).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

H. A. Haus, Waves and Fields in Optoelectronics (Prentice-Hall, 1984).

X. Han, T. Wang, B. Liu, Y. He, and Y. Zhu, “Tunable triple plasmon-induced transparencies in dual T-shaped cavities side-coupled waveguide,” IEEE Photon. Technol. Lett. 28, 347–350 (2016).

[Crossref]

X. Han, T. Wang, X. Li, B. Liu, Y. He, and J. Tang, “Ultrafast and low-power dynamically tunable plasmon-induced transparencies in compact aperture-coupled rectangular resonators,” J. Lightwave Technol. 33, 5133–5139 (2015).

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

Z. Chai, X. Hu, H. Yang, and Q. Gong, “All-optical tunable on-chip plasmon-induced transparency based on two surface-plasmon-polaritons absorption,” Appl. Phys. Lett. 108, 151104 (2016).

[Crossref]

Y. Zhu, X. Hu, H. Yang, and Q. Gong, “On-chip plasmon-induced transparency based on plasmonic coupled nanocavities,” Sci. Rep. 4, 3752 (2014).

[Crossref]

Z. Chai, X. Hu, Y. Zhu, S. Sun, H. Yang, and Q. Gong, “Ultracompact chip-integrated electromagnetically induced transparency in a single plasmonic composite nanocavity,” Adv. Opt. Mater. 2, 320–325 (2014).

[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, 211104 (2013).

[Crossref]

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

[Crossref]

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

[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 nanophotonic devices,” arXiv:1809.03555 (2018).

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

[Crossref]

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

[Crossref]

P. B. Johnson and R. Christy, “Optical constants of the noble metals,” Phys. Rev. B 6, 4370–4379 (1972).

[Crossref]

K. Kojima, B. Wang, U. Kamilov, T. Koike-Akino, and K. Parsons, “Acceleration of FDTD-based inverse design using a neural network approach,” in Integrated Photonics Research, Silicon and Nanophotonics (Optical Society of America, 2017), paper ITu1A.4.

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

[Crossref]

A. B. Khanikaev, C. Wu, and G. Shvets, “Fano-resonant metamaterials and their applications,” Nanophotonics 2, 247–264 (2013).

[Crossref]

C. Wu, A. B. Khanikaev, and G. Shvets, “Broadband slow light metamaterial based on a double-continuum Fano resonance,” Phys. Rev. Lett. 106, 107403 (2011).

[Crossref]

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

[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 nanophotonic devices,” arXiv:1809.03555 (2018).

K. Kojima, B. Wang, U. Kamilov, T. Koike-Akino, and K. Parsons, “Acceleration of FDTD-based inverse design using a neural network approach,” in Integrated Photonics Research, Silicon and Nanophotonics (Optical Society of America, 2017), paper ITu1A.4.

K. Kojima, B. Wang, U. Kamilov, T. Koike-Akino, and K. Parsons, “Acceleration of FDTD-based inverse design using a neural network approach,” in Integrated Photonics Research, Silicon and Nanophotonics (Optical Society of America, 2017), paper ITu1A.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 nanophotonic devices,” arXiv:1809.03555 (2018).

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, 2812–2819 (2018).

[Crossref]

E. Bor, O. Alparslan, M. Turduev, Y. S. Hanay, H. Kurt, S. I. Arakawa, and M. Murata, “Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter,” Opt. Express 26, 29032–29044 (2018).

[Crossref]

E. Bor, C. Babayigit, H. Kurt, K. Staliunas, and M. Turduev, “Directional invisibility by genetic optimization,” Opt. Lett. 43, 5781–5784 (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, 374–377 (2015).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv:1611.01578 (2016).

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]

P.-H. Fu, S.-C. Lo, P.-C. Tsai, K.-L. Lee, and P.-K. Wei, “Optimization for gold nanostructure-based surface plasmon biosensors using a microgenetic algorithm,” ACS Photon. 5, 2320–2327 (2018).

[Crossref]

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “A generative model for inverse design of metamaterials,” arXiv:1805.10181 (2018).

Z. He, H. Li, S. Zhan, G. Cao, and B. Li, “Combined theoretical analysis for plasmon-induced transparency in waveguide systems,” Opt. Lett. 39, 5543–5546 (2014).

[Crossref]

Z. Zhang, L. Zhang, H. Li, and H. Chen, “Plasmon induced transparency in a surface plasmon polariton waveguide with a comb line slot and rectangle cavity,” Appl. Phys. Lett. 104, 231114 (2014).

[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, 211104 (2013).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (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, 5142–5149 (2017).

[Crossref]

X. Han, T. Wang, X. Li, B. Liu, Y. He, and J. Tang, “Ultrafast and low-power dynamically tunable plasmon-induced transparencies in compact aperture-coupled rectangular resonators,” J. Lightwave Technol. 33, 5133–5139 (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, 31945–31955 (2015).

[Crossref]

L. Chen, T. Zhang, X. Li, and W. P. Huang, “Novel hybrid plasmonic waveguide consisting of two identical dielectric nanowires symmetrically placed on each side of a thin metal film,” Opt. Express 20, 20535–20544 (2012).

[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 nanophotonic devices,” arXiv:1809.03555 (2018).

X. Han, T. Wang, B. Liu, Y. He, and Y. Zhu, “Tunable triple plasmon-induced transparencies in dual T-shaped cavities side-coupled waveguide,” IEEE Photon. Technol. Lett. 28, 347–350 (2016).

[Crossref]

X. Han, T. Wang, X. Li, B. Liu, Y. He, and J. Tang, “Ultrafast and low-power dynamically tunable plasmon-induced transparencies in compact aperture-coupled rectangular resonators,” J. Lightwave Technol. 33, 5133–5139 (2015).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photon. 5, 1365–1369 (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, 211104 (2013).

[Crossref]

S. Zhang, D. A. Genov, Y. Wang, M. Liu, and X. Zhang, “Plasmon-induced transparency in metamaterials,” Phys. Rev. Lett. 101, 047401 (2008).

[Crossref]

H. Lu, X. Liu, and D. Mao, “Plasmonic analog of electromagnetically induced transparency in multi-nanoresonator-coupled waveguide systems,” Phys. Rev. A 85, 053803 (2012).

[Crossref]

H. Lu, X. Liu, D. Mao, and G. Wang, “Plasmonic nanosensor based on Fano resonance in waveguide-coupled resonators,” Opt. Lett. 37, 3780–3782 (2012).

[Crossref]

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

[Crossref]

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “A generative model for inverse design of metamaterials,” arXiv:1805.10181 (2018).

P.-H. Fu, S.-C. Lo, P.-C. Tsai, K.-L. Lee, and P.-K. Wei, “Optimization for gold nanostructure-based surface plasmon biosensors using a microgenetic algorithm,” ACS Photon. 5, 2320–2327 (2018).

[Crossref]

H. Lu, X. Gan, D. Mao, and J. Zhao, “Graphene-supported manipulation of surface plasmon polaritons in metallic nanowaveguides,” Photon. Res. 5, 162–167 (2017).

[Crossref]

H. Lu, X. Liu, D. Mao, and G. Wang, “Plasmonic nanosensor based on Fano resonance in waveguide-coupled resonators,” Opt. Lett. 37, 3780–3782 (2012).

[Crossref]

H. Lu, X. Liu, and D. Mao, “Plasmonic analog of electromagnetically induced transparency in multi-nanoresonator-coupled waveguide systems,” Phys. Rev. A 85, 053803 (2012).

[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, 374–377 (2015).

[Crossref]

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

[Crossref]

I. Malkiel, A. Nagler, M. Mrejen, U. Arieli, L. Wolf, and H. Suchowski, “Deep learning for design and retrieval of nano-photonic structures,” arXiv:1702.07949 (2017).

H. Lu, X. Gan, D. Mao, and J. Zhao, “Graphene-supported manipulation of surface plasmon polaritons in metallic nanowaveguides,” Photon. Res. 5, 162–167 (2017).

[Crossref]

H. Lu, X. Liu, D. Mao, and G. Wang, “Plasmonic nanosensor based on Fano resonance in waveguide-coupled resonators,” Opt. Lett. 37, 3780–3782 (2012).

[Crossref]

H. Lu, X. Liu, and D. Mao, “Plasmonic analog of electromagnetically induced transparency in multi-nanoresonator-coupled waveguide systems,” Phys. Rev. A 85, 053803 (2012).

[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, 378–382 (2015).

[Crossref]

K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,” Evol. Comput. 10, 99–127 (2002).

[Crossref]

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

[Crossref]

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

[Crossref]

I. Malkiel, A. Nagler, M. Mrejen, U. Arieli, L. Wolf, and H. Suchowski, “Deep learning for design and retrieval of nano-photonic structures,” arXiv:1702.07949 (2017).

I. Malkiel, A. Nagler, M. Mrejen, U. Arieli, L. Wolf, and H. Suchowski, “Deep learning for design and retrieval of nano-photonic structures,” arXiv:1702.07949 (2017).

B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” arXiv:1611.02167 (2016).

A. Devarakonda, M. Naumov, and M. Garland, “AdaBatch: adaptive batch sizes for training deep neural networks,” arXiv:1712.02029 (2017).

M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, “Deep neural network inverse design of integrated nanophotonic devices,” arXiv:1809.03555 (2018).

K. Kojima, B. Wang, U. Kamilov, T. Koike-Akino, and K. Parsons, “Acceleration of FDTD-based inverse design using a neural network approach,” in Integrated Photonics Research, Silicon and Nanophotonics (Optical Society of America, 2017), paper ITu1A.4.

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, 374–377 (2015).

[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, M. Tegmark, J. D. Joannopoulos, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (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, 374–377 (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, 378–382 (2015).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

M. Qin, L. Wang, X. Zhai, D. Chen, and S. Xia, “Generating and manipulating high quality factors of Fano resonance in nanoring resonator by stacking a half nanoring,” Nano. Res. Lett. 12, 578 (2017).

[Crossref]

B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” arXiv:1611.02167 (2016).

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “A generative model for inverse design of metamaterials,” arXiv:1805.10181 (2018).

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: a review of Bayesian optimization,” Proc. IEEE 104, 148–175 (2016).

[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, 378–382 (2015).

[Crossref]

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

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

A. B. Khanikaev, C. Wu, and G. Shvets, “Fano-resonant metamaterials and their applications,” Nanophotonics 2, 247–264 (2013).

[Crossref]

C. Wu, A. B. Khanikaev, and G. Shvets, “Broadband slow light metamaterial based on a double-continuum Fano resonance,” Phys. Rev. Lett. 106, 107403 (2011).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

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

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,” Evol. Comput. 10, 99–127 (2002).

[Crossref]

I. Malkiel, A. Nagler, M. Mrejen, U. Arieli, L. Wolf, and H. Suchowski, “Deep learning for design and retrieval of nano-photonic structures,” arXiv:1702.07949 (2017).

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, 211104 (2013).

[Crossref]

Z. Chai, X. Hu, Y. Zhu, S. Sun, H. Yang, and Q. Gong, “Ultracompact chip-integrated electromagnetically induced transparency in a single plasmonic composite nanocavity,” Adv. Opt. Mater. 2, 320–325 (2014).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

Z. Yu, H. Cui, and X. Sun, “Genetically optimized on-chip wideband ultracompact reflectors and Fabry-Perot cavities,” Photon. Res. 5, B15–B19 (2017).

[Crossref]

H. Cui, X. Sun, and Z. Yu, “Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint,” Opt. Lett. 42, 3093–3096 (2017).

[Crossref]

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: a review of Bayesian optimization,” Proc. IEEE 104, 148–175 (2016).

[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 nanophotonic devices,” arXiv:1809.03555 (2018).

G. C. Cawley and N. L. Talbot, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” J. Mach. Learn. Res. 11, 2079–2107 (2010).

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

[Crossref]

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

[Crossref]

G. Carleo and M. Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science 355, 602–606 (2017).

[Crossref]

P.-H. Fu, S.-C. Lo, P.-C. Tsai, K.-L. Lee, and P.-K. Wei, “Optimization for gold nanostructure-based surface plasmon biosensors using a microgenetic algorithm,” ACS Photon. 5, 2320–2327 (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, 2812–2819 (2018).

[Crossref]

E. Bor, C. Babayigit, H. Kurt, K. Staliunas, and M. Turduev, “Directional invisibility by genetic optimization,” Opt. Lett. 43, 5781–5784 (2018).

[Crossref]

E. Bor, O. Alparslan, M. Turduev, Y. S. Hanay, H. Kurt, S. I. Arakawa, and M. Murata, “Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter,” Opt. Express 26, 29032–29044 (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, 374–377 (2015).

[Crossref]

K. Kojima, B. Wang, U. Kamilov, T. Koike-Akino, and K. Parsons, “Acceleration of FDTD-based inverse design using a neural network approach,” in Integrated Photonics Research, Silicon and Nanophotonics (Optical Society of America, 2017), paper ITu1A.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 nanophotonic devices,” arXiv:1809.03555 (2018).

M. Qin, L. Wang, X. Zhai, D. Chen, and S. Xia, “Generating and manipulating high quality factors of Fano resonance in nanoring resonator by stacking a half nanoring,” Nano. Res. Lett. 12, 578 (2017).

[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, 211104 (2013).

[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, 378–382 (2015).

[Crossref]

X. Han, T. Wang, B. Liu, Y. He, and Y. Zhu, “Tunable triple plasmon-induced transparencies in dual T-shaped cavities side-coupled waveguide,” IEEE Photon. Technol. Lett. 28, 347–350 (2016).

[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, 31945–31955 (2015).

[Crossref]

X. Han, T. Wang, X. Li, B. Liu, Y. He, and J. Tang, “Ultrafast and low-power dynamically tunable plasmon-induced transparencies in compact aperture-coupled rectangular resonators,” J. Lightwave Technol. 33, 5133–5139 (2015).

[Crossref]

T. Wang, Y. Zhang, Z. Hong, and Z. Han, “Analogue of electromagnetically induced transparency in integrated plasmonics with radiative and subradiant resonators,” Opt. Express 22, 21529–21534 (2014).

[Crossref]

S. Zhang, D. A. Genov, Y. Wang, M. Liu, and X. Zhang, “Plasmon-induced transparency in metamaterials,” Phys. Rev. Lett. 101, 047401 (2008).

[Crossref]

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: a review of Bayesian optimization,” Proc. IEEE 104, 148–175 (2016).

[Crossref]

P.-H. Fu, S.-C. Lo, P.-C. Tsai, K.-L. Lee, and P.-K. Wei, “Optimization for gold nanostructure-based surface plasmon biosensors using a microgenetic algorithm,” ACS Photon. 5, 2320–2327 (2018).

[Crossref]

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

[Crossref]

I. Malkiel, A. Nagler, M. Mrejen, U. Arieli, L. Wolf, and H. Suchowski, “Deep learning for design and retrieval of nano-photonic structures,” arXiv:1702.07949 (2017).

A. B. Khanikaev, C. Wu, and G. Shvets, “Fano-resonant metamaterials and their applications,” Nanophotonics 2, 247–264 (2013).

[Crossref]

C. Wu, A. B. Khanikaev, and G. Shvets, “Broadband slow light metamaterial based on a double-continuum Fano resonance,” Phys. Rev. Lett. 106, 107403 (2011).

[Crossref]

M. Qin, L. Wang, X. Zhai, D. Chen, and S. Xia, “Generating and manipulating high quality factors of Fano resonance in nanoring resonator by stacking a half nanoring,” Nano. Res. Lett. 12, 578 (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, 5142–5149 (2017).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (2017).

[Crossref]

Z. Chai, X. Hu, H. Yang, and Q. Gong, “All-optical tunable on-chip plasmon-induced transparency based on two surface-plasmon-polaritons absorption,” Appl. Phys. Lett. 108, 151104 (2016).

[Crossref]

Z. Chai, X. Hu, Y. Zhu, S. Sun, H. Yang, and Q. Gong, “Ultracompact chip-integrated electromagnetically induced transparency in a single plasmonic composite nanocavity,” Adv. Opt. Mater. 2, 320–325 (2014).

[Crossref]

Y. Zhu, X. Hu, H. Yang, and Q. Gong, “On-chip plasmon-induced transparency based on plasmonic coupled nanocavities,” Sci. Rep. 4, 3752 (2014).

[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Canorenteria, B. Delacy, M. Tegmark, J. D. Joannopoulos, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (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, 5142–5149 (2017).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (2017).

[Crossref]

Z. Chen and L. Yu, “Multiple Fano resonances based on different waveguide modes in a symmetry breaking plasmonic system,” IEEE Photon. J. 6, 4802208 (2014).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photon. 5, 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, 3093–3096 (2017).

[Crossref]

Z. Yu, H. Cui, and X. Sun, “Genetically optimized on-chip wideband ultracompact reflectors and Fabry-Perot cavities,” Photon. Res. 5, B15–B19 (2017).

[Crossref]

M. Qin, L. Wang, X. Zhai, D. Chen, and S. Xia, “Generating and manipulating high quality factors of Fano resonance in nanoring resonator by stacking a half nanoring,” Nano. Res. Lett. 12, 578 (2017).

[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, 211104 (2013).

[Crossref]

Z. Zhang, L. Zhang, H. Li, and H. Chen, “Plasmon induced transparency in a surface plasmon polariton waveguide with a comb line slot and rectangle cavity,” Appl. Phys. Lett. 104, 231114 (2014).

[Crossref]

S. Zhang, D. A. Genov, Y. Wang, M. Liu, and X. Zhang, “Plasmon-induced transparency in metamaterials,” Phys. Rev. Lett. 101, 047401 (2008).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (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, 5142–5149 (2017).

[Crossref]

L. Chen, T. Zhang, X. Li, and W. P. Huang, “Novel hybrid plasmonic waveguide consisting of two identical dielectric nanowires symmetrically placed on each side of a thin metal film,” Opt. Express 20, 20535–20544 (2012).

[Crossref]

S. Zhang, D. A. Genov, Y. Wang, M. Liu, and X. Zhang, “Plasmon-induced transparency in metamaterials,” Phys. Rev. Lett. 101, 047401 (2008).

[Crossref]

Z. Zhang, L. Zhang, H. Li, and H. Chen, “Plasmon induced transparency in a surface plasmon polariton waveguide with a comb line slot and rectangle cavity,” Appl. Phys. Lett. 104, 231114 (2014).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (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, 5142–5149 (2017).

[Crossref]

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “A generative model for inverse design of metamaterials,” arXiv:1805.10181 (2018).

X. Han, T. Wang, B. Liu, Y. He, and Y. Zhu, “Tunable triple plasmon-induced transparencies in dual T-shaped cavities side-coupled waveguide,” IEEE Photon. Technol. Lett. 28, 347–350 (2016).

[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, 31945–31955 (2015).

[Crossref]

Y. Zhu, X. Hu, H. Yang, and Q. Gong, “On-chip plasmon-induced transparency based on plasmonic coupled nanocavities,” Sci. Rep. 4, 3752 (2014).

[Crossref]

Z. Chai, X. Hu, Y. Zhu, S. Sun, H. Yang, and Q. Gong, “Ultracompact chip-integrated electromagnetically induced transparency in a single plasmonic composite nanocavity,” Adv. Opt. Mater. 2, 320–325 (2014).

[Crossref]

B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv:1611.01578 (2016).

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

[Crossref]

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

[Crossref]

P.-H. Fu, S.-C. Lo, P.-C. Tsai, K.-L. Lee, and P.-K. Wei, “Optimization for gold nanostructure-based surface plasmon biosensors using a microgenetic algorithm,” ACS Photon. 5, 2320–2327 (2018).

[Crossref]

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

[Crossref]

Z. Chai, X. Hu, Y. Zhu, S. Sun, H. Yang, and Q. Gong, “Ultracompact chip-integrated electromagnetically induced transparency in a single plasmonic composite nanocavity,” Adv. Opt. Mater. 2, 320–325 (2014).

[Crossref]

Z. Zhang, L. Zhang, H. Li, and H. Chen, “Plasmon induced transparency in a surface plasmon polariton waveguide with a comb line slot and rectangle cavity,” Appl. Phys. Lett. 104, 231114 (2014).

[Crossref]

Z. Chai, X. Hu, H. Yang, and Q. Gong, “All-optical tunable on-chip plasmon-induced transparency based on two surface-plasmon-polaritons absorption,” Appl. Phys. Lett. 108, 151104 (2016).

[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, 211104 (2013).

[Crossref]

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

K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,” Evol. Comput. 10, 99–127 (2002).

[Crossref]

P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geosci. Remote Sens. Lett. 12, 309–313 (2015).

[Crossref]

Z. Chen and L. Yu, “Multiple Fano resonances based on different waveguide modes in a symmetry breaking plasmonic system,” IEEE Photon. J. 6, 4802208 (2014).

[Crossref]

X. Han, T. Wang, B. Liu, Y. He, and Y. Zhu, “Tunable triple plasmon-induced transparencies in dual T-shaped cavities side-coupled waveguide,” IEEE Photon. Technol. Lett. 28, 347–350 (2016).

[Crossref]

X. Han, T. Wang, X. Li, B. Liu, Y. He, and J. Tang, “Ultrafast and low-power dynamically tunable plasmon-induced transparencies in compact aperture-coupled rectangular resonators,” J. Lightwave Technol. 33, 5133–5139 (2015).

[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, 5142–5149 (2017).

[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, 2812–2819 (2018).

[Crossref]

G. C. Cawley and N. L. Talbot, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” J. Mach. Learn. Res. 11, 2079–2107 (2010).

M. Qin, L. Wang, X. Zhai, D. Chen, and S. Xia, “Generating and manipulating high quality factors of Fano resonance in nanoring resonator by stacking a half nanoring,” Nano. Res. Lett. 12, 578 (2017).

[Crossref]

A. B. Khanikaev, C. Wu, and G. Shvets, “Fano-resonant metamaterials and their applications,” Nanophotonics 2, 247–264 (2013).

[Crossref]

D. K. Gramotnev and S. I. Bozhevolnyi, “Plasmonics beyond the diffraction limit,” Nat. Photonics 4, 83–91 (2010).

[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, 374–377 (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, 378–382 (2015).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]

E. Bor, O. Alparslan, M. Turduev, Y. S. Hanay, H. Kurt, S. I. Arakawa, and M. Murata, “Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter,” Opt. Express 26, 29032–29044 (2018).

[Crossref]

T. Wang, Y. Zhang, Z. Hong, and Z. Han, “Analogue of electromagnetically induced transparency in integrated plasmonics with radiative and subradiant resonators,” Opt. Express 22, 21529–21534 (2014).

[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, 16866–16873 (2016).

[Crossref]

T. Zhang, J. Dai, Y. Dai, Y. Fan, X. Han, J. Li, F. Yin, Y. Zhou, and K. Xu, “Dynamically tunable plasmon induced absorption in graphene-assisted metallodielectric grating,” Opt. Express 25, 26221–26233 (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, 31945–31955 (2015).

[Crossref]

L. Chen, T. Zhang, X. Li, and W. P. Huang, “Novel hybrid plasmonic waveguide consisting of two identical dielectric nanowires symmetrically placed on each side of a thin metal film,” Opt. Express 20, 20535–20544 (2012).

[Crossref]

Z. He, H. Li, S. Zhan, G. Cao, and B. Li, “Combined theoretical analysis for plasmon-induced transparency in waveguide systems,” Opt. Lett. 39, 5543–5546 (2014).

[Crossref]

H. Cui, X. Sun, and Z. Yu, “Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint,” Opt. Lett. 42, 3093–3096 (2017).

[Crossref]

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, 3868–3871 (2016).

[Crossref]

E. Bor, C. Babayigit, H. Kurt, K. Staliunas, and M. Turduev, “Directional invisibility by genetic optimization,” Opt. Lett. 43, 5781–5784 (2018).

[Crossref]

H. Lu, X. Liu, D. Mao, and G. Wang, “Plasmonic nanosensor based on Fano resonance in waveguide-coupled resonators,” Opt. Lett. 37, 3780–3782 (2012).

[Crossref]

A. Ahmadivand, R. Sinha, B. Gerislioglu, M. Karabiyik, N. Pala, and M. Shur, “Transition from capacitive coupling to direct charge transfer in asymmetric terahertz plasmonic assemblies,” Opt. Lett. 41, 5333–5336 (2016).

[Crossref]

Z. Yu, H. Cui, and X. Sun, “Genetically optimized on-chip wideband ultracompact reflectors and Fabry-Perot cavities,” Photon. Res. 5, B15–B19 (2017).

[Crossref]

H. Lu, X. Gan, D. Mao, and J. Zhao, “Graphene-supported manipulation of surface plasmon polaritons in metallic nanowaveguides,” Photon. Res. 5, 162–167 (2017).

[Crossref]

H. Lu, X. Liu, and D. Mao, “Plasmonic analog of electromagnetically induced transparency in multi-nanoresonator-coupled waveguide systems,” Phys. Rev. A 85, 053803 (2012).

[Crossref]

P. B. Johnson and R. Christy, “Optical constants of the noble metals,” Phys. Rev. B 6, 4370–4379 (1972).

[Crossref]

C. Wu, A. B. Khanikaev, and G. Shvets, “Broadband slow light metamaterial based on a double-continuum Fano resonance,” Phys. Rev. Lett. 106, 107403 (2011).

[Crossref]

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

[Crossref]

S. Zhang, D. A. Genov, Y. Wang, M. Liu, and X. Zhang, “Plasmon-induced transparency in metamaterials,” Phys. Rev. Lett. 101, 047401 (2008).

[Crossref]

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: a review of Bayesian optimization,” Proc. IEEE 104, 148–175 (2016).

[Crossref]

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

[Crossref]

Y. Zhu, X. Hu, H. Yang, and Q. Gong, “On-chip plasmon-induced transparency based on plasmonic coupled nanocavities,” Sci. Rep. 4, 3752 (2014).

[Crossref]

G. Carleo and M. Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science 355, 602–606 (2017).

[Crossref]

K. Kojima, B. Wang, U. Kamilov, T. Koike-Akino, and K. Parsons, “Acceleration of FDTD-based inverse design using a neural network approach,” in Integrated Photonics Research, Silicon and Nanophotonics (Optical Society of America, 2017), paper ITu1A.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 nanophotonic devices,” arXiv:1809.03555 (2018).

H. A. Haus, Waves and Fields in Optoelectronics (Prentice-Hall, 1984).

B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv:1611.01578 (2016).

I. Malkiel, A. Nagler, M. Mrejen, U. Arieli, L. Wolf, and H. Suchowski, “Deep learning for design and retrieval of nano-photonic structures,” arXiv:1702.07949 (2017).

Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “A generative model for inverse design of metamaterials,” arXiv:1805.10181 (2018).

B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” arXiv:1611.02167 (2016).

G. Fishman, Monte Carlo: Concepts, Algorithms, and Applications (Springer, 2013).

A. Devarakonda, M. Naumov, and M. Garland, “AdaBatch: adaptive batch sizes for training deep neural networks,” arXiv:1712.02029 (2017).

https://scikit-learn.org/stable/ .

A. Chipperfield and P. Fleming, “The MATLAB genetic algorithm toolbox,” in IEEE Colloquium on Applied Control Techniques Using MATLAB (1995).