An overview of deep-learning models for metasurface design and optimization

Muhammad Fizan, Sadia Noureen, Muhammad Zubair, Muhammad Qasim Mehmood, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Deep Neural Networks (DNNs) have emerged as a powerful tool for predicting the structure and composition of diverse nanophotonic devices based on their desired response. These techniques have played a pivotal role in driving advancements across a spectrum of fields within optics and photonics. Notably, they have significantly contributed to the progress and innovation observed in the domains of plasmonics, holography, chirality, topological photonics, airy beams, color filters, vortex beams, and absorbers. This paper reviews the most recent advances in using Machine Learning (ML) and Deep Learning (DL) for inverse design of nanophotonic devices. In the past, conventional optimization techniques have been used as a design tool to optimize the metasurface and nanodevice structures but in recent years ML and DL based techniques have revolutionized this process. These techniquese are more time efficient and accurate.

Original languageEnglish (US)
Title of host publicationNanophotonics and Micro/Nano Optics IX
EditorsZhiping Zhou, Kazumi Wada, Limin Tong
ISBN (Electronic)9781510667952
StatePublished - 2023
EventNanophotonics and Micro/Nano Optics IX 2023 - Beijing, China
Duration: Oct 14 2023Oct 16 2023

Publication series

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


ConferenceNanophotonics and Micro/Nano Optics IX 2023


  • chirality
  • Deep Learning (DL)
  • Deep Neural Networks (DNN)
  • holography
  • Machine Learning (ML)
  • nanophotonic devices
  • plasmonics

ASJC Scopus subject areas

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


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