Nanophotonic particle simulation and inverse design using artificial neural networks

John Peurifoy; Yichen Shen; Li Jing; Yi Yang; Fidel Cano-Renteria; Brendan Delacy; Max Tegmark; John D. Joannopoulos; Marin Soljačić

doi:10.1117/12.2289195

23 February 2018 Nanophotonic particle simulation and inverse design using artificial neural networks

John Peurifoy, Yichen Shen, Li Jing, Yi Yang, Fidel Cano-Renteria, Brendan Delacy, Max Tegmark, John D. Joannopoulos, Marin Soljačić

Author Affiliations +

Proceedings Volume 10526, Physics and Simulation of Optoelectronic Devices XXVI; 1052607 (2018) https://doi.org/10.1117/12.2289195
Event: SPIE OPTO, 2018, San Francisco, California, United States

Abstract

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used solve nanophotonic inverse design problems by using back-propogation - where the gradient is analytical, not numerical.

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John Peurifoy, Yichen Shen, Li Jing, Yi Yang, Fidel Cano-Renteria, Brendan Delacy, Max Tegmark, John D. Joannopoulos, and Marin Soljačić "Nanophotonic particle simulation and inverse design using artificial neural networks", Proc. SPIE 10526, Physics and Simulation of Optoelectronic Devices XXVI, 1052607 (23 February 2018); https://doi.org/10.1117/12.2289195

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