@inproceedings{8ea1670f499a41feaa56056baebd4a20,
title = "An overview of deep-learning models for metasurface design and optimization",
abstract = "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.",
keywords = "chirality, Deep Learning (DL), Deep Neural Networks (DNN), holography, Machine Learning (ML), nanophotonic devices, plasmonics",
author = "Muhammad Fizan and Sadia Noureen and Muhammad Zubair and Mehmood, {Muhammad Qasim} and Yehia Massoud",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE. All rights reserved.; Nanophotonics and Micro/Nano Optics IX 2023 ; Conference date: 14-10-2023 Through 16-10-2023",
year = "2023",
doi = "10.1117/12.2686202",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zhiping Zhou and Kazumi Wada and Limin Tong",
booktitle = "Nanophotonics and Micro/Nano Optics IX",
address = "United States",
}