23 February 2018 Nanophotonic particle simulation and inverse design using artificial neural networks
Author Affiliations +
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John Peurifoy, John Peurifoy, Yichen Shen, Yichen Shen, Li Jing, Li Jing, Yi Yang, Yi Yang, Fidel Cano-Renteria, Fidel Cano-Renteria, Brendan Delacy, Brendan Delacy, Max Tegmark, Max Tegmark, John D. Joannopoulos, John D. Joannopoulos, Marin Soljačić, 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); doi: 10.1117/12.2289195; https://doi.org/10.1117/12.2289195

Back to Top