Year
Month
(Peer-Reviewed) AI-assisted metaphotonics
Minsung Kang ¹, Seokju Choi ², Kaixi Fu ¹, Xiaoyuan Liu ³, Zhun Wei 魏准 ⁴, Lei Jin 晋蕾 ⁵, Hao Wang 王浩 ⁶ ⁷, Olivier J. F. Martin ³, Joel K. W. Yang ⁸, Sunae So ², Trevon Badloe ¹ ⁹ ¹⁰
¹ Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
² Department of Control and Instrumentation Engineering, Korea University, Sejong 30019, Republic of Korea
³ Nanophotonics and Metrology Laboratory (NAM), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne 1015, Switzerland
⁴ Innovation Institute of Electromagnetic Information and Electronics Integration, Zhejiang Key Laboratory of Intelligent – Electromagnetic Control and Advanced Electronic Integration, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310017, China
中国 杭州 浙江大学信息科学与电子工程学院 电磁信息与电子集成创新研究所 全省电磁智能感控与先进电子集成重点实验室
⁵ Key Laboratory of RF Circuits & System of Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Xiasha High Education Park, Hangzhou 310018, China
中国 杭州 下沙高教园区 杭州电子科技大学电子信息学院 射频电路与系统教育部重点实验室
⁶ School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
中国 北京 北京航空航天大学仪器科学与光电工程学院
⁷ Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China
中国 杭州 北京航空航天大学杭州创新研究院
⁸ Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
⁹ Division of Smart Energy Convergence Engineering, Korea University, Sejong 30019, Republic of Korea
¹⁰ Digital Healthcare Center, Sejong Institute for Business and Technology, Korea University, Sejong 30019, Republic of Korea
Opto-Electronic Advances, 2026-04-17
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.
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  • Polarization-guided diffusion prior for eyeglass reflection removal                                Interpretable low-dose CT enhancement via multi-Gaussian cluster variance reduction
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