@inproceedings{2955932013574f2980107b7059948f4e,
title = "Accelerating Electromagnetic Inverse-Design using Deep Learning",
abstract = "One of the primary ways that deep learning has been applied to electro magnetics in recent years is for accelerating inverse design. We present one such method for designing metasurface supercells which are robust to structural erosion and dilation, a typical variety of nanofabrication error. A pair of deep neural networks are trained to high accuracy to predict diffraction efficiencies from a supercell mask, and then evaluated exhaustively to find tolerance bounds for freeform supercell designs.",
author = "Jenkins, {Ronald P.} and Campbell, {Sawyer D.} and Werner, {Pingjuan L.} and Werner, {Douglas H.}",
note = "Funding Information: This research was supported in EXTREME (contract HR00111720032). Publisher Copyright: {\textcopyright} 2022 European Association for Antennas and Propagation.; 16th European Conference on Antennas and Propagation, EuCAP 2022 ; Conference date: 27-03-2022 Through 01-04-2022",
year = "2022",
language = "English (US)",
series = "2022 16th European Conference on Antennas and Propagation, EuCAP 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 16th European Conference on Antennas and Propagation, EuCAP 2022",
address = "United States",
}