Nanophotonic particle simulation and inverse design using artificial neural networks
Name
eaar4206.full.pdf
Description
Published version
Size
393.26 KB
Format
Adobe PDF
Checksum (MD5)
18af49811b64832a067a754090ab6049
Author(s)
Peurifoy, John
Shen, Yichen
Jing, Li
Yang, Yi
Cano-Renteria, Fidel
DeLacy, Brendan G
Joannopoulos, John D
Tegmark, Max
Soljačić, Marin
Date Issued
2018
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Version
Final published version
Abstract
Copyright © 2018 The Authors. 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.
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
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/SCIADV.AAR4206
