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.
AI-assisted metaphotonics_1
AI-assisted metaphotonics_2
AI-assisted metaphotonics_3
  • Multi-dimensional photodetection: from material intrinsic properties and metasurface engineering to silicon photonic integration
  • Wenqi Liu, Zilan Tang, Qingzhao Hua, Liang Liu, Xiaoxia Wang, Anlian Pan
  • Opto-Electronic Technology
  • 2026-03-20
  • Holotomography-driven learning unlocks in-silico staining of single cells in flow cytometry by avoiding fluorescence co-registration
  • Daniele Pirone, Giusy Giugliano, Michela Schiavo, Annalaura Montella, Martina Mugnano, Vincenza Cerbone, Maddalena Raia, Giulia Scalia Ivana Kurelac, Diego Luis Medina, Lisa Miccio Mario Capasso, Achille Iolascon, Pasquale Memmolo, Pietro Ferraro
  • Opto-Electronic Science
  • 2026-02-25
  • Narrow beam and low-sidelobe electro-optic beam steering on thin-film lithium niobate optical phased array
  • Yang Li, Shiyao Deng, Xiao Ma, Ziliang Fang, Shufeng Li Weikang Xu, Fangheng Fu, Xu Ouyang, Yuming Wei, Tiefeng Yang Heyuan Guan, Huihui Lu
  • Opto-Electronic Science
  • 2026-02-25
  • Scene-level passive polarization 3D imaging
  • Xin Wang, Pingli Han, Xiyuan Luo, Qianqian Liu, Tong Zhang, Xue Dong, Meng Xiang, Jinpeng Liu, Yanyan Liu, Fei Liu
  • Opto-Electronic Advances
  • 2026-02-12
  • Modelling-guided inverse design strategy for semitransparent perovskite photovoltaics with customized colors
  • Seok-Beom Seo, Rira Kang, Eun-Joo Lee, So-Yeon Ju, Min Jae Lee, Byunghong Lee, Sun-Kyung Kim
  • Opto-Electronic Advances
  • 2026-02-12
  • A hybrid integrated high-precision tunable semiconductor laser
  • Yiran Zhu, Botao Fu, Zhiwei Fang, Qiyue Hu, Jianping Yu, Yunpeng Song, Yu Ma, Min Wang, Kunpeng Jia, Zhenda Xie, Ya Cheng
  • Opto-Electronic Advances
  • 2026-02-12
  • Soft chiral superstructure enabled dynamic polychromatic holography
  • Chun-Ting Xu, Lu Li, Quan-Ming Chen, Guang-Yao Wang, Wei Hu
  • Opto-Electronic Advances
  • 2026-02-12
  • Millisecond-level electrically switchable metalens for adaptive rotational depth mapping and diffraction-limited imaging
  • Yeseul Kim, Jihae Lee, Won-Sik Kim, Hyeonsu Heo, Dongmin Jeon, Beomha Yang, Xiaotong Li, Harit Keawmuang, Shiqi Hu, Young-Ki Kim, Trevon Badloe, Junsuk Rho
  • Opto-Electronic Advances
  • 2026-02-12
  • Ambient-energy-driven space-time-coding metasurface for space-frequency-division multiplexing wireless communications
  • Han Wei Tian, Chao Song, Dong Jie Wang, Qian Zhu, Tie Jun Cui, Wei Xiang Jiang
  • Opto-Electronic Advances
  • 2026-02-12
  • Ultra-sensitive multi-band infrared polarization photodetector based on 1T'-MoTe₂/2H-MoTe₂ van der Waals heterostructure
  • Yuting Pan, Lidan Lu, Bofei Zhu, Chunhua An, Jing Yu, Guanghui Ren, Jian Zhen Ou, Mingli Dong, Zheng You, Lianqing Zhu
  • Opto-Electronic Advances
  • 2026-02-09
  • Tunable compound eyes with coaxial lens-on-lens ommatidia for cooperative bi-focal imaging
  • Zhi-Juan Sun, Wei-Jian Zhong, Qing Cai, Yi-Fan Lu, Chang-Xu Li, Dong-Dong Han, Yong-Lai Zhang
  • Opto-Electronic Advances
  • 2026-02-09
  • High-efficiency infrared upconversion imaging with nonlinear silicon metasurfaces empowered by quasi-bound states in the continuum
  • Tingting Liu, Jumin Qiu, Meibao Qin, Xu Tu Huifu Qiu, Feng Wu, Tianbao Yu, Qiegen Liu, Shuyuan Xiao
  • Opto-Electronic Advances
  • 2026-01-29



  • Polarization-guided diffusion prior for eyeglass reflection removal        Interpretable low-dose CT enhancement via multi-Gaussian cluster variance reduction
    About
    |
    Contact
    |
    Copyright © PubCard