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arXiv · 1712.03222

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 Soljacic
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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, Marin Soljacic. 2017-10-18. Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks. https://arxiv.org/abs/1712.03222

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