Abstract

Artificial neural networks (ANNs) have been recognized as a fast and flexible tool for microwave modeling, design, and optimization. Similarly, ANNs can be utilized in the design and optimization of photonic devices to provide fast simulation and speed up the design process. ANN models can produce great efficiency in design and optimization processes when repetitive computationally intensive simulations are required for modeling of optical passive elements using commercial tools. In this paper we explore how trained ANNs can be utilized for the fast simulation of optical passive elements, while attaining high accuracy. We present the design of four fundamental optical passive elements using commercial tools and compare the results with our trained ANN model. In all four examples illustrated, the error ranges from 0.5% to 1.7% while the simulation time is in the range of milliseconds instead of minutes or hours. Finally, we discuss possible uses of these trained ANNs with respect to the design, optimization, and statistical validation of photonic devices.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Designing integrated photonic devices using artificial neural networks

Alec M. Hammond and Ryan M. Camacho
Opt. Express 27(21) 29620-29638 (2019)

Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

Tian Zhang, Jia Wang, Qi Liu, Jinzan Zhou, Jian Dai, Xu Han, Yue Zhou, and Kun Xu
Photon. Res. 7(3) 368-380 (2019)

Accelerating silicon photonic parameter extraction using artificial neural networks

Alec M. Hammond, Easton Potokar, and Ryan M. Camacho
OSA Continuum 2(6) 1964-1973 (2019)

References

  • View by:
  • |
  • |
  • |

  1. D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
    [Crossref]
  2. A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
    [Crossref]
  3. OptiFDTD, https://www.optiwave.com .
  4. Lumerical FDTD Solutions, https://www.lumerical.com .
  5. COMSOL Multiphysics, https://www.comsol.com .
  6. F. Wang, V. K. Devabhaktuni, C. Xi, and Q.-J. Zhang, “Neural network structures and training algorithms for RF and microwave applications,” Int. J. RF Microwave Comput. Aid. Eng. 9, 216–240 (1999).
    [Crossref]
  7. Q.-J. Zhang, K. Gupta, and V. Devabhaktuni, “Artificial neural networks for RF and microwave design-from theory to practice,” IEEE Trans. Microwave Theory Tech. 51, 1339–1350 (2003).
    [Crossref]
  8. S. W. S. Wan, L. Z. L. Zhang, and Q. Z. Q. Zhang, “Application of artificial neural networks for electromagnetic modeling and computational electromagnetics,” in 51st Midwest Symposium on Circuits and Systems (2008), pp. 743–746.
  9. T. Abreu-Cerqueira, A. Dourado-Sisnando, and V. F. Rodriguez-Esquerre, “Analysis and design of directional couplers based on AlxGa1–xAs by using an efficient neural networks: a design tool simulation implemented in C/C++,” in SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) (IEEE, 2011), pp. 881–885.
  10. M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
    [Crossref]
  11. R. R. Andrawis, M. A. Swillam, M. A. El-Gamal, and E. A. Soliman, “Artificial neural network modeling of plasmonic transmission lines,” Appl. Opt. 55, 2780–2790 (2016).
    [Crossref]
  12. H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
    [Crossref]
  13. M. M. Vai, S. Wu, B. Li, and S. Prasad, “Reverse modeling of microwave circuits with bidirectional neural network models,” IEEE Trans. Microwave Theory Tech. 46, 1492–1494 (1998).
    [Crossref]
  14. Q. J. Zhang and K. C. Gupta, Neural Networks for RF and Microwave Design (Book + Neuromodeler Disk), 1st ed. (Artech House, 2000).
  15. S. K. Selvaraja, P. Jaenen, W. Bogaerts, D. V. Thourhout, P. Dumon, and R. Baets, “Fabrication of photonic wire and crystal circuits in silicon-on-insulator using 193-nm optical lithography,” J. Lightwave Technol. 27, 4076–4083 (2009).
    [Crossref]
  16. D. Dai and S. He, “Analysis of characteristics of bent rib waveguides,” J. Opt. Soc. Am. A 21, 113–121 (2004).
    [Crossref]
  17. M. K. Smit, E. C. Pennings, and H. Blok, “Normalized approach to the design of low-loss optical waveguide bends,” J. Lightwave Technol. 11, 1737–1742 (1993).
    [Crossref]
  18. R. Syms and J. Cozens, Optical Guided Waves and Devices (McGraw-Hill, 1992).
  19. L. Chrostowski and M. Hochberg, Silicon Photonics Design (Cambridge University, 2015).
  20. L. B. Soldano and E. C. Pennings, “Optical multi-mode interference devices based on self-imaging: principles and applications,” J. Lightwave Technol. 13, 615–627 (1995).
    [Crossref]
  21. S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vuckovic, and A. W. Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12, 659–670 (2018).
    [Crossref]
  22. K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” arXiv:1810.11709 (2018).

2018 (1)

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

2016 (2)

R. R. Andrawis, M. A. Swillam, M. A. El-Gamal, and E. A. Soliman, “Artificial neural network modeling of plasmonic transmission lines,” Appl. Opt. 55, 2780–2790 (2016).
[Crossref]

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

2014 (1)

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

2009 (2)

M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
[Crossref]

S. K. Selvaraja, P. Jaenen, W. Bogaerts, D. V. Thourhout, P. Dumon, and R. Baets, “Fabrication of photonic wire and crystal circuits in silicon-on-insulator using 193-nm optical lithography,” J. Lightwave Technol. 27, 4076–4083 (2009).
[Crossref]

2008 (1)

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]

2004 (1)

2003 (1)

Q.-J. Zhang, K. Gupta, and V. Devabhaktuni, “Artificial neural networks for RF and microwave design-from theory to practice,” IEEE Trans. Microwave Theory Tech. 51, 1339–1350 (2003).
[Crossref]

1999 (1)

F. Wang, V. K. Devabhaktuni, C. Xi, and Q.-J. Zhang, “Neural network structures and training algorithms for RF and microwave applications,” Int. J. RF Microwave Comput. Aid. Eng. 9, 216–240 (1999).
[Crossref]

1998 (1)

M. M. Vai, S. Wu, B. Li, and S. Prasad, “Reverse modeling of microwave circuits with bidirectional neural network models,” IEEE Trans. Microwave Theory Tech. 46, 1492–1494 (1998).
[Crossref]

1995 (1)

L. B. Soldano and E. C. Pennings, “Optical multi-mode interference devices based on self-imaging: principles and applications,” J. Lightwave Technol. 13, 615–627 (1995).
[Crossref]

1993 (1)

M. K. Smit, E. C. Pennings, and H. Blok, “Normalized approach to the design of low-loss optical waveguide bends,” J. Lightwave Technol. 11, 1737–1742 (1993).
[Crossref]

Abo El Maaty, M. I.

M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
[Crossref]

Abreu-Cerqueira, T.

T. Abreu-Cerqueira, A. Dourado-Sisnando, and V. F. Rodriguez-Esquerre, “Analysis and design of directional couplers based on AlxGa1–xAs by using an efficient neural networks: a design tool simulation implemented in C/C++,” in SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) (IEEE, 2011), pp. 881–885.

Al-Begain, K.

M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
[Crossref]

Andrawis, R. R.

Baets, R.

Blok, H.

M. K. Smit, E. C. Pennings, and H. Blok, “Normalized approach to the design of low-loss optical waveguide bends,” J. Lightwave Technol. 11, 1737–1742 (1993).
[Crossref]

Boeuf, F.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Bogaerts, W.

Bowers, J. E.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Cassan, E.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Chen, K. K.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Chrostowski, L.

L. Chrostowski and M. Hochberg, Silicon Photonics Design (Cambridge University, 2015).

Cozens, J.

R. Syms and J. Cozens, Optical Guided Waves and Devices (McGraw-Hill, 1992).

Dai, D.

Devabhaktuni, V.

Q.-J. Zhang, K. Gupta, and V. Devabhaktuni, “Artificial neural networks for RF and microwave design-from theory to practice,” IEEE Trans. Microwave Theory Tech. 51, 1339–1350 (2003).
[Crossref]

Devabhaktuni, V. K.

F. Wang, V. K. Devabhaktuni, C. Xi, and Q.-J. Zhang, “Neural network structures and training algorithms for RF and microwave applications,” Int. J. RF Microwave Comput. Aid. Eng. 9, 216–240 (1999).
[Crossref]

Dourado-Sisnando, A.

T. Abreu-Cerqueira, A. Dourado-Sisnando, and V. F. Rodriguez-Esquerre, “Analysis and design of directional couplers based on AlxGa1–xAs by using an efficient neural networks: a design tool simulation implemented in C/C++,” in SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) (IEEE, 2011), pp. 881–885.

Duan, N.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Dumon, P.

El-Gamal, M. A.

Fang, Q.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Fédéli, J.-M.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Gupta, K.

Q.-J. Zhang, K. Gupta, and V. Devabhaktuni, “Artificial neural networks for RF and microwave design-from theory to practice,” IEEE Trans. Microwave Theory Tech. 51, 1339–1350 (2003).
[Crossref]

Gupta, K. C.

Q. J. Zhang and K. C. Gupta, Neural Networks for RF and Microwave Design (Book + Neuromodeler Disk), 1st ed. (Artech House, 2000).

Hameed, M. F. O.

M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
[Crossref]

Hartmann, J.-M.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

He, S.

Hochberg, M.

L. Chrostowski and M. Hochberg, Silicon Photonics Design (Cambridge University, 2015).

Jaenen, P.

Jin, W.

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

Kabir, H.

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]

Komljenovic, T.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Li, B.

M. M. Vai, S. Wu, B. Li, and S. Prasad, “Reverse modeling of microwave circuits with bidirectional neural network models,” IEEE Trans. Microwave Theory Tech. 46, 1492–1494 (1998).
[Crossref]

Li, C.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Lim, A. E. J.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Lin, Z.

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

Liow, T. Y.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Marris-Morini, D.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Mashanovich, G. Z.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Molesky, S.

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

Nasr, A. M.

M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
[Crossref]

Nedeljkovic, M.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

O’Brien, P.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Obayya, S. S. A.

M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
[Crossref]

Pennings, E. C.

L. B. Soldano and E. C. Pennings, “Optical multi-mode interference devices based on self-imaging: principles and applications,” J. Lightwave Technol. 13, 615–627 (1995).
[Crossref]

M. K. Smit, E. C. Pennings, and H. Blok, “Normalized approach to the design of low-loss optical waveguide bends,” J. Lightwave Technol. 11, 1737–1742 (1993).
[Crossref]

Piggott, A. Y.

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

Prasad, S.

M. M. Vai, S. Wu, B. Li, and S. Prasad, “Reverse modeling of microwave circuits with bidirectional neural network models,” IEEE Trans. Microwave Theory Tech. 46, 1492–1494 (1998).
[Crossref]

Reed, G. T.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Rodriguez, A. W.

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

Rodriguez-Esquerre, V. F.

T. Abreu-Cerqueira, A. Dourado-Sisnando, and V. F. Rodriguez-Esquerre, “Analysis and design of directional couplers based on AlxGa1–xAs by using an efficient neural networks: a design tool simulation implemented in C/C++,” in SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) (IEEE, 2011), pp. 881–885.

Schmid, J. H.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Selvaraja, S. K.

Smit, M. K.

M. K. Smit, E. C. Pennings, and H. Blok, “Normalized approach to the design of low-loss optical waveguide bends,” J. Lightwave Technol. 11, 1737–1742 (1993).
[Crossref]

Soldano, L. B.

L. B. Soldano and E. C. Pennings, “Optical multi-mode interference devices based on self-imaging: principles and applications,” J. Lightwave Technol. 13, 615–627 (1995).
[Crossref]

Soliman, E. A.

Song, J.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Swillam, M. A.

Syms, R.

R. Syms and J. Cozens, Optical Guided Waves and Devices (McGraw-Hill, 1992).

Tern, R. P. C.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Thomson, D.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Thourhout, D. V.

Tu, X.

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

Unni, R.

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” arXiv:1810.11709 (2018).

Vai, M. M.

M. M. Vai, S. Wu, B. Li, and S. Prasad, “Reverse modeling of microwave circuits with bidirectional neural network models,” IEEE Trans. Microwave Theory Tech. 46, 1492–1494 (1998).
[Crossref]

Virot, L.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Vivien, L.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Vuckovic, J.

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

Wan, S. W. S.

S. W. S. Wan, L. Z. L. Zhang, and Q. Z. Q. Zhang, “Application of artificial neural networks for electromagnetic modeling and computational electromagnetics,” in 51st Midwest Symposium on Circuits and Systems (2008), pp. 743–746.

Wang, F.

F. Wang, V. K. Devabhaktuni, C. Xi, and Q.-J. Zhang, “Neural network structures and training algorithms for RF and microwave applications,” Int. J. RF Microwave Comput. Aid. Eng. 9, 216–240 (1999).
[Crossref]

Wang, Y.

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]

Wu, S.

M. M. Vai, S. Wu, B. Li, and S. Prasad, “Reverse modeling of microwave circuits with bidirectional neural network models,” IEEE Trans. Microwave Theory Tech. 46, 1492–1494 (1998).
[Crossref]

Xi, C.

F. Wang, V. K. Devabhaktuni, C. Xi, and Q.-J. Zhang, “Neural network structures and training algorithms for RF and microwave applications,” Int. J. RF Microwave Comput. Aid. Eng. 9, 216–240 (1999).
[Crossref]

Xu, D.-X.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Yao, K.

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” arXiv:1810.11709 (2018).

Yu, M.

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]

Zhang, L. Z. L.

S. W. S. Wan, L. Z. L. Zhang, and Q. Z. Q. Zhang, “Application of artificial neural networks for electromagnetic modeling and computational electromagnetics,” in 51st Midwest Symposium on Circuits and Systems (2008), pp. 743–746.

Zhang, Q.

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]

Zhang, Q. J.

Q. J. Zhang and K. C. Gupta, Neural Networks for RF and Microwave Design (Book + Neuromodeler Disk), 1st ed. (Artech House, 2000).

Zhang, Q. Z. Q.

S. W. S. Wan, L. Z. L. Zhang, and Q. Z. Q. Zhang, “Application of artificial neural networks for electromagnetic modeling and computational electromagnetics,” in 51st Midwest Symposium on Circuits and Systems (2008), pp. 743–746.

Zhang, Q.-J.

Q.-J. Zhang, K. Gupta, and V. Devabhaktuni, “Artificial neural networks for RF and microwave design-from theory to practice,” IEEE Trans. Microwave Theory Tech. 51, 1339–1350 (2003).
[Crossref]

F. Wang, V. K. Devabhaktuni, C. Xi, and Q.-J. Zhang, “Neural network structures and training algorithms for RF and microwave applications,” Int. J. RF Microwave Comput. Aid. Eng. 9, 216–240 (1999).
[Crossref]

Zheng, Y.

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” arXiv:1810.11709 (2018).

Zilkie, A.

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

Appl. Opt. (1)

IEEE J. Sel. Top. Quantum Electron. (1)

A. E. J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P. C. Tern, and T. Y. Liow, “Review of silicon photonics foundry efforts,” IEEE J. Sel. Top. Quantum Electron. 20, 405–416 (2014).
[Crossref]

IEEE Trans. Microwave Theory Tech. (3)

Q.-J. Zhang, K. Gupta, and V. Devabhaktuni, “Artificial neural networks for RF and microwave design-from theory to practice,” IEEE Trans. Microwave Theory Tech. 51, 1339–1350 (2003).
[Crossref]

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]

M. M. Vai, S. Wu, B. Li, and S. Prasad, “Reverse modeling of microwave circuits with bidirectional neural network models,” IEEE Trans. Microwave Theory Tech. 46, 1492–1494 (1998).
[Crossref]

Int. J. RF Microwave Comput. Aid. Eng. (1)

F. Wang, V. K. Devabhaktuni, C. Xi, and Q.-J. Zhang, “Neural network structures and training algorithms for RF and microwave applications,” Int. J. RF Microwave Comput. Aid. Eng. 9, 216–240 (1999).
[Crossref]

J. Lightwave Technol. (3)

M. K. Smit, E. C. Pennings, and H. Blok, “Normalized approach to the design of low-loss optical waveguide bends,” J. Lightwave Technol. 11, 1737–1742 (1993).
[Crossref]

L. B. Soldano and E. C. Pennings, “Optical multi-mode interference devices based on self-imaging: principles and applications,” J. Lightwave Technol. 13, 615–627 (1995).
[Crossref]

S. K. Selvaraja, P. Jaenen, W. Bogaerts, D. V. Thourhout, P. Dumon, and R. Baets, “Fabrication of photonic wire and crystal circuits in silicon-on-insulator using 193-nm optical lithography,” J. Lightwave Technol. 27, 4076–4083 (2009).
[Crossref]

J. Opt. (1)

D. Thomson, A. Zilkie, J. E. Bowers, T. Komljenovic, G. T. Reed, L. Vivien, D. Marris-Morini, E. Cassan, L. Virot, J.-M. Fédéli, J.-M. Hartmann, J. H. Schmid, D.-X. Xu, F. Boeuf, P. O’Brien, G. Z. Mashanovich, and M. Nedeljkovic, “Roadmap on silicon photonics,” J. Opt. 18, 073003 (2016).
[Crossref]

J. Opt. Soc. Am. A (1)

Nat. Photonics (1)

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

Opt. Quantum Electron. (1)

M. F. O. Hameed, S. S. A. Obayya, K. Al-Begain, A. M. Nasr, and M. I. Abo El Maaty, “Accurate radial basis function based neural network approach for analysis of photonic crystal fibers,” Opt. Quantum Electron. 40, 891–905 (2009).
[Crossref]

Other (9)

K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” arXiv:1810.11709 (2018).

R. Syms and J. Cozens, Optical Guided Waves and Devices (McGraw-Hill, 1992).

L. Chrostowski and M. Hochberg, Silicon Photonics Design (Cambridge University, 2015).

Q. J. Zhang and K. C. Gupta, Neural Networks for RF and Microwave Design (Book + Neuromodeler Disk), 1st ed. (Artech House, 2000).

S. W. S. Wan, L. Z. L. Zhang, and Q. Z. Q. Zhang, “Application of artificial neural networks for electromagnetic modeling and computational electromagnetics,” in 51st Midwest Symposium on Circuits and Systems (2008), pp. 743–746.

T. Abreu-Cerqueira, A. Dourado-Sisnando, and V. F. Rodriguez-Esquerre, “Analysis and design of directional couplers based on AlxGa1–xAs by using an efficient neural networks: a design tool simulation implemented in C/C++,” in SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) (IEEE, 2011), pp. 881–885.

OptiFDTD, https://www.optiwave.com .

Lumerical FDTD Solutions, https://www.lumerical.com .

COMSOL Multiphysics, https://www.comsol.com .

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 (17)

Fig. 1.
Fig. 1. Typical multi-layer perceptron neural network structure. A multi-layer perceptron network consists of an input layer, one or more hidden layers, and an output layer.
Fig. 2.
Fig. 2. Two typical SOI-based silicon photonics waveguides. (a) Strip and (b) rib.
Fig. 3.
Fig. 3. Effective refractive index as a function of waveguide width, ( w g ), for strip waveguide ( t s = 0 ) where silicon thickness, ( t g ), is 220 nm. (a) Training data from Lumerical MODE (triangles) and OptiMODE (squares) mode solvers for different wavelengths. (b) Comparison between the trained ANN model (solid lines) and simulation data from Lumerical MODE (triangles) and OptiMODE (squares) for wavelengths of 1310 and 1550 nm.
Fig. 4.
Fig. 4. Effective index as a function of wavelength for a ridge waveguide ( t s = 90 nm ), where silicon thickness is 220 nm. (a) Training data from both Lumerical MODE (triangles) and OptiMODE (squares) mode solvers. (b) ANN model (solid lines) in comparison with Lumerical MODE (triangles) and OptiMODE (squares) for different silicon waveguide widths.
Fig. 5.
Fig. 5. Relative error comparing ANN output with Lumerical MODE and OptiMODE simulation results. Error is less than 0.65% for output data within the training range and has a maximum of 1.4% for specific output but not within the specified ANN input range.
Fig. 6.
Fig. 6. 90° bend waveguide. The core silicon layer has a thickness of 220 nm with a buried oxide layer of 2 μm.
Fig. 7.
Fig. 7. Bending loss in dB of fundamental (a) TE and (b) TM modes as a function of bending radius as obtained using the trained ANN.
Fig. 8.
Fig. 8. Bending loss in dB of fundamental TE mode as a function of bending radius as obtained using the trained ANN for waveguide widths of 385, 477, and 565 nm. The Lumerical FDTD simulation results (circle markers) are compared with the ANN model (dashed line) and a fifth-order polynomial (dotted line).
Fig. 9.
Fig. 9. Directional coupler.
Fig. 10.
Fig. 10. Cross-over length, L x , versus directional coupler gap calculated using the ANN model.
Fig. 11.
Fig. 11. Transmission in cross-over port as a function of coupler length and gap.
Fig. 12.
Fig. 12. Cross-over transmission as a function of coupler length as obtained using Lumerical FDTD and trained ANN. Note: these plots are vertical slices through the surface presented in Fig. 11.
Fig. 13.
Fig. 13. Schematic of an MMI coupler.
Fig. 14.
Fig. 14. MMI transmission as a function of the MMI length for TE modes after an EME sweep. The red circle represents the maximum transmission which occurs at the optimum MMI length.
Fig. 15.
Fig. 15. Maximum transmission for a 1 × 2 MMI coupler ( h co = 220 nm ) as a function of w c and w g for fundamental TE mode obtained from the ANN model.
Fig. 16.
Fig. 16. Maximum transmission as a function of w c for three w g : 0.5 μm, 0.73 μm, and 1 μm. The ANN model (solid lines) is compared with Lumerical simulations (markers).
Fig. 17.
Fig. 17. Optimum MMI length for a 1 × 2 MMI coupler ( h co = 220 nm ) as a function of w c for TE modes. Comparison between the ANN model (red line), EME simulation (blue circles), and theoretical results (dashed green line).

Tables (1)

Tables Icon

Table 1. Parameter Space for the Waveguide Simulation

Equations (9)

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

E T ( w ) = 1 2 k T j = 1 m | y j ( x k , w ) d j k | 2 ,
B L = 10 log ( P out P in ) ,
κ 2 = P cross P in = sin 2 ( c L c ) ,
c = π Δ n λ = π 2 L x ,
κ 2 = sin 2 ( π L c 2 L x ) = 1 2 1 2 cos ( π L c L x ) .
κ 2 = y + A sin ( 2 π T L c + ϕ ] .
β 0 β v = v ( v + 2 ) π 3 L π ,
L π = π β 0 β 1 .
L c = 3 L π 4 N

Metrics