Neural Networks and Reservoir ComputingPhotonic and Optical DevicesRandom lasers and scattering media

Minsung Kang, Seokjun Choi, Kaixiu Fu, Xiaoyuan Liu, Zhun Wei, Lei Jin, Hao Wang, Olivier J F Martin, Joel K. W. Yang, Sunae So, Trevon Badloe

2026.1.1Opto-Electronic Advances

DOI: 10.29026/oea.2026.250263

tlooto Summary

This review provides a comprehensive overview of state-of-the-art AI-driven approaches for metaphotonic systems and focuses 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.

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 inter-section of metaphotonics and AI.

Citation format

KANG, Minsung, et al. AI-assisted metaphotonics. Opto-Electronic Advances, 2026, 9(4): 250263.