@inproceedings{ca5f6f31ce7f4e9cbe9e553d00f60aab,
title = "Optimization and Deep Learning Techniques for Nanophotonic Inverse-Design",
abstract = "Metamaterial and metasurface devices (i.e., meta-devices) have shown tremendous potential for disrupting conventional RF and optical system design due to their ability to tailor the propagation of electromagnetic radiation in a desired fashion. Meta-devices are generally synthesized from {"}meta-atom{"}building blocks which are optimized to meet a certain set of user-designed performance criteria. Meta-atom optimization often requires the use of full-wave electromagnetic solvers which can make the process computationally challenging, especially when a large number of design parameters are used to define the meta-atoms. To this end, inverse-design strategies based on multi-objective optimization and deep learning which seek to efficiently explore the vast space afforded by nanofabricated meta-devices are presented.",
author = "Campbell, {S. D.} and Jenkins, {R. P.} and Whiting, {E. B.} and Werner, {P. L.} and Werner, {D. H.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 16th International Congress on Artificial Materials for Novel Wave Phenomena, Metamaterials 2022 ; Conference date: 12-09-2022 Through 17-09-2022",
year = "2022",
doi = "10.1109/Metamaterials54993.2022.9920895",
language = "English (US)",
series = "2022 16th International Congress on Artificial Materials for Novel Wave Phenomena, Metamaterials 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "X086--X088",
booktitle = "2022 16th International Congress on Artificial Materials for Novel Wave Phenomena, Metamaterials 2022",
address = "United States",
}