Discovering metasurface robustness through deep learning

Ronald P. Jenkins, Sawyer D. Campbell, Douglas H. Werner, Pingjuan L. Werner

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

Abstract

Deep learning has recently become an important part of nanophotonic device design, with many researchers leveraging the power of neural networks to aid in inverse-design analysis. Acting as surrogate models for full-wave solvers, neural networks are now employed to develop new metasurface elements and gain insight into the underlying physics which dictate their behavior. A new avenue of discovery, which has been facilitated by the recent developments in deep learning, lies in the domain of metasurface robustness, a subject that presents many challenges to traditional solvers. Characterizing even relatively simple designs with a full-wave solver may be computationally expensive enough to make optimization challenging or even intractable. On the other hand, robustness must be measured by introducing some form of tolerance analysis into an optimization. Structures must be perturbed many times over, perhaps even in an exhaustive fashion. When the process is further complicated by considering complex design parameterizations (with many degrees of freedom), these full-wave optimizations are no longer tractable. However, deep neural networks can help to counter these challenges owing to their GPU speedup and powerful learning characteristics. By evaluating many full-wave metasurface designs ahead-oftime and investing them into neural network training, the network can then be used in tolerance analysis for substantial speedups down the road. This work showcases how we designed a suitable neural network for this purpose, as well as several studies conducted using this deep learning platform in this important area of metasurface robustness.

Original languageEnglish (US)
Title of host publicationMetamaterials, Metadevices, and Metasystems 2021
EditorsNader Engheta, Mikhail A. Noginov, Nikolay I. Zheludev
PublisherSPIE
ISBN (Electronic)9781510644281
DOIs
StatePublished - 2021
EventMetamaterials, Metadevices, and Metasystems 2021 - San Diego, United States
Duration: Aug 1 2021Aug 5 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11795
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceMetamaterials, Metadevices, and Metasystems 2021
Country/TerritoryUnited States
CitySan Diego
Period8/1/218/5/21

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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