Data driven approaches in nanophotonics: A review of AI-enabled metadevices

Abstract

Data-driven approaches have revolutionized the design and optimization of photonic metadevices by harnessing advanced artificial intelligence methodologies. This review takes a model-centric perspective that synthesizes emerging design strategies and delineates how traditional trial-and-error and computationally intensive electromagnetic simulations are being supplanted by deep learning frameworks that efficiently navigate expansive design spaces. We discuss artificial intelligence implementation in several metamaterial design aspects from high-degree-of-freedom design to large language model-assisted design. By addressing challenges such as transformer model implementation, fabrication limitations, and intricate mutual coupling effects, these AI-enabled strategies not only streamline the forward modeling process but also offer robust pathways for the realization of multifunctional and fabrication-friendly nanophotonic devices. This review further highlights emerging opportunities and persistent challenges, setting the stage for next-generation strategies in nanophotonic engineering.

Article information

Article type
Review Article
Submitted
15 May 2025
Accepted
26 Sep 2025
First published
26 Sep 2025
This article is Open Access
Creative Commons BY-NC license

Nanoscale, 2025, Accepted Manuscript

Data driven approaches in nanophotonics: A review of AI-enabled metadevices

H. Zhang, L. Kang, S. D. Campbell, J. T. Young and D. H. Werner, Nanoscale, 2025, Accepted Manuscript , DOI: 10.1039/D5NR02043C

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