Nanophotonic particle simulation and inverse design using artificial neural networks
Author(s)
Peurifoy, John
Shen, Yichen
Jing, Li
Yang, Yi
Cano-Renteria, Fidel
Delacy, Brendan
Tegmark, Max Erik
Joannopoulos, John
Soljacic, Marin
Date Issued
2016
Journal
Physics and Simulation of Optoelectronic Devices XXVI
Publisher
SPIE-Intl Soc Optical Eng
Citation
Cano-Renteria, Fidel, Max Tegmark, Marin Soljacic, John D. Joannopoulos, John Peurifoy, Yichen Shen, Li Jing, Yi Yang, and Brendan G. DeLacy. “Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks.” Edited by Marek Osiński, Yasuhiko Arakawa, and Bernd Witzigmann. Physics and Simulation of Optoelectronic Devices XXVI (February 23, 2018). doi:10.1117/12.2289195.
Version
Final published version
Abstract
© 2018 SPIE. 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1117/12.2289195
