Deep Learning for Nanoscale Arbitrary Meta-element Robustness

Ronald P. Jenkins, Philip J. O'Connor, Sawyer D. Campbell, Pingjuan L. Werner, Douglas H. Werner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1695-1696
Number of pages2
ISBN (Electronic)9781728166704
DOIs
StatePublished - Jul 5 2020
Event2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Virtually, Toronto, Canada
Duration: Jul 5 2020Jul 10 2020

Publication series

Name2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings

Conference

Conference2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020
CountryCanada
CityVirtually, Toronto
Period7/5/207/10/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Instrumentation

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