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Nanophotonic particle simulation and inverse design using artificial neural networks.

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.

SUBMITTER: Peurifoy J

PROVIDER: S-EPMC5983917 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Nanophotonic particle simulation and inverse design using artificial neural networks.

Peurifoy John J Shen Yichen Y Jing Li L Yang Yi Y Cano-Renteria Fidel F DeLacy Brendan G BG Joannopoulos John D JD Tegmark Max M Soljačić Marin M

Science advances 20180601 6

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 propagati ...[more]

PMID: 29868640

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