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

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

doi:10.1117/12.2686202

28 November 2023 An overview of deep-learning models for metasurface design and optimization

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

Proceedings Volume 12773, Nanophotonics and Micro/Nano Optics IX; 127731G (2023) https://doi.org/10.1117/12.2686202
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

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 techniques are more time efficient and accurate.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Muhammad Fizan, Sadia Noureen, Muhammad Zubair, Muhammad Qasim Mehmood, and Yehia Massoud "An overview of deep-learning models for metasurface design and optimization", Proc. SPIE 12773, Nanophotonics and Micro/Nano Optics IX, 127731G (28 November 2023); https://doi.org/10.1117/12.2686202

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