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Nanophotonic particle simulation and inverse design using artificial neural networks

Science Advances · 2018 · Vol. 4(6) · pp. eaar4206–eaar4206

John Peurifoy✉(Massachusetts Institute of Technology)Yichen Shen✉(Massachusetts Institute of Technology)Jing Li(Massachusetts Institute of Technology)Yi Yang(Massachusetts Institute of Technology)Fidel Cano-Renteria(Massachusetts Institute of Technology)Brendan G. DeLacy(United States Army)John D. Joannopoulos(Massachusetts Institute of Technology)Max Tegmark(Massachusetts Institute of Technology)Marin Soljačić(Massachusetts Institute of Technology)

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Abstract

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data 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 to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.

Photonic and Optical Devices Photonic Crystals and Applications Neural Networks and Reservoir ComputingNanophotonicsComputer scienceArtificial neural networkInverseArtificial intelligenceNanotechnologyMaterials scienceMathematics