AI-assisted metaphotonics

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Authors: Minsung Kang, Seokju Choi, Kaixi Fu, Xiaoyuan Liu, Zhun Wei, Lei Jin, Hao Wang, Olivier J. F. Martin, Joel K. W. Yang, Sunae So, Trevon Badloe
Cited by: 7
References: 195

Abstract

The convergence of artificial intelligence (AI) and metaphotonics is creating a new paradigm for controlling light-matter interactions. The synergy of AI's ability to learn complex relationships in multidimensional data and provide ultra-fast inference with the capacity of metaphotonics to engineer optical properties not found in nature is unlocking a new era in computational design, real-time control, and fully automated optical systems. This review provides a comprehensive overview of state-of-the-art AI-driven approaches for metaphotonic systems. We focus on the solutions to real-world problems in accelerating metaphotonic simulations and inverse design, optical data characterization, and the development of fully integrated end-to-end AI-assisted metaphotonic systems. Finally, we provide our perspectives on the future research directions and emerging opportunities at the rapidly evolving intersection of metaphotonics and AI.

Fields of Study & Subjects

Neural Networks and Reservoir Computing Photonic and Optical Devices Random lasers and scattering media

Publication details

Journal / source
Opto-Electronic Advances
Publisher
Opto-Electronic Advances
Volume
9
Issue
4
Pages
250263–250263
Publication date
1 January 2026
Language
EN