Deep Learning and Optimization for Efficient and Robust Nanophotonic Design

Research output: Contribution to journalConference articlepeer-review


Nanophotonic device design requires efficient full-wave solvers and optimization techniques. To this end, deep learning shows promise for improving both forward- and inverse-design aspects of such problems. Meanwhile, multiobjective and topology optimization approaches have proven successful in realizing highly performant freeform nanophotonic devices with tailored functionalities. Combining deep learning with advanced optimization techniques gives designers beyond state-of-the-art tools for realizing extremely efficient nanophotonic device design cycles.

Original languageEnglish (US)
Pages (from-to)1059-1060
Number of pages2
JournalInternational Conference on Metamaterials, Photonic Crystals and Plasmonics
StatePublished - 2023
Event13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023 - Paris, France
Duration: Jul 18 2023Jul 21 2023

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Materials Science (miscellaneous)
  • Electronic, Optical and Magnetic Materials
  • Materials Chemistry

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