@inproceedings{f55d8a7c1bb34460b9a06063e4310b47,
title = "Deep Learning for Nanoscale Arbitrary Meta-element Robustness",
abstract = "Optical metasurface designs often suffer performance degradations originating from the fabrication process due to manufacturing uncertainties. Therefore, a critical need exists for designs which incorporate robustness, but this is typically intractable during optimization due to the large number of function evaluations required. Nevertheless, by augmenting the process with a deep learning intermediate step, the speed of function evaluations can be greatly accelerated, enabling robustness to be directly optimized during the inverse design process rather than as an a posteriori step applied after the fact. A deep learning assisted robustness framework is introduced for application to nanophotonic meta-element design.",
author = "Jenkins, {Ronald P.} and O'Connor, {Philip J.} and Campbell, {Sawyer D.} and Werner, {Pingjuan L.} and Werner, {Douglas H.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 ; Conference date: 05-07-2020 Through 10-07-2020",
year = "2020",
month = jul,
day = "5",
doi = "10.1109/IEEECONF35879.2020.9329496",
language = "English (US)",
series = "2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1695--1696",
booktitle = "2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings",
address = "United States",
}