Accelerated Optimization of Robust Nanophotonic Devices via Deep Learning

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

Abstract

The potential for nanophotonic devices to disrupt existing and create new commercial applications has led to a surge in design and manufacturing research in recent years. Yet, new technologies must not only demonstrate performance advantages over legacy solutions, but also improved fabrication cost and reliability. Therefore, inverse-design techniques which optimize guaranteed performance in the presence of fabrication uncertainties are needed to maximize the yield achievable within a given process window. However, simulating a nominal structure and all potential perturbations caused by a variety of highly complex and coupled fabrication uncertainties results in a combinatorial explosion of solutions that ultimately makes direct optimization intractable. To overcome this, we exploit deep learning and demonstrate a neural network that accurately predicts the performance of a representative metasurface supercell in the presence of fabrication uncertainties. The trained neural network is subsequently paired with a modified multi-objective optimization procedure which enables one to study the tradeoffs between nominal performance and guaranteed performance.

Original languageEnglish (US)
Title of host publication2024 Photonics North, PN 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350366365
DOIs
StatePublished - 2024
Event2024 Photonics North, PN 2024 - Vancouver, Canada
Duration: May 28 2024May 30 2024

Publication series

Name2024 Photonics North, PN 2024

Conference

Conference2024 Photonics North, PN 2024
Country/TerritoryCanada
CityVancouver
Period5/28/245/30/24

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Hardware and Architecture
  • Atomic and Molecular Physics, and Optics

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