中国物理B ›› 2026, Vol. 35 ›› Issue (8): 80702-080702.doi: 10.1088/1674-1056/ae6b40

• • 上一篇    

Reviews of algorithm-driven terahertz metamaterials: From intelligent design to multidisciplinary applications

Wenyue Cao(曹文钺)1,2,3, Yuying Jiang(蒋玉英)1,2,4, Hongyi Ge(葛宏义)1,2,3,†, and Juncheng Cao(曹俊诚)5,‡   

  1. 1 Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China;
    2 Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, Zhengzhou 450001, China;
    3 College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China;
    4 School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China;
    5 State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
  • 收稿日期:2026-04-22 修回日期:2026-05-08 接受日期:2026-05-11 发布日期:2026-07-23
  • 通讯作者: Hongyi Ge, Juncheng Cao E-mail:gehongyi2004@163.com;jccao@mail.sim.ac.cn
  • 基金资助:
    This work was supported by the National Key R&D Program of China (Grant No. 2023YFB3210300) and the National Natural Science Foundation of China (Grant Nos. 12333012 and 62271191).

Reviews of algorithm-driven terahertz metamaterials: From intelligent design to multidisciplinary applications

Wenyue Cao(曹文钺)1,2,3, Yuying Jiang(蒋玉英)1,2,4, Hongyi Ge(葛宏义)1,2,3,†, and Juncheng Cao(曹俊诚)5,‡   

  1. 1 Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China;
    2 Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, Zhengzhou 450001, China;
    3 College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China;
    4 School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China;
    5 State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
  • Received:2026-04-22 Revised:2026-05-08 Accepted:2026-05-11 Published:2026-07-23
  • Contact: Hongyi Ge, Juncheng Cao E-mail:gehongyi2004@163.com;jccao@mail.sim.ac.cn
  • Supported by:
    This work was supported by the National Key R&D Program of China (Grant No. 2023YFB3210300) and the National Natural Science Foundation of China (Grant Nos. 12333012 and 62271191).

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摘要： Terahertz metamaterials, composed of subwavelength artificial structures, exhibit strongly nonlinear and tightly coupled electromagnetic responses governed by geometry, material properties, and resonance modes. Driven by growing demands in high-sensitivity sensing, terahertz communication, and functional imaging, the design space of these devices has rapidly expanded, rendering conventional empirical and parameter-sweeping approaches inefficient for global optimization. Algorithm-driven strategies, particularly those leveraging machine learning and deep learning, have emerged as powerful surrogates for electromagnetic simulation, enabling automated multi-objective optimization and rapid inverse design. Beyond predictive capabilities, these algorithms uncover latent physical mechanisms and support adaptive, closed-loop experimental implementations. Within a unified framework, this review systematically categorizes and summarizes advances in algorithm-driven design of terahertz metamaterials, including traditional optimization methods, machine learning, deep learning, and reinforcement learning, highlighting their roles in multi-layer structural design, multimodal coupling control, and dynamic multi-target detection. Finally, we conduct a detailed analysis of current challenges, including data quality, model generalization, and physical interpretability, and clearly identify future research directions. These directions are expected to lead to practical applications in high-sensitivity chemical and biological detection, terahertz wireless communication, and wave-based functional imaging.

关键词: terahertz metamaterials, AI algorithms, intelligent design, applications

Abstract: Terahertz metamaterials, composed of subwavelength artificial structures, exhibit strongly nonlinear and tightly coupled electromagnetic responses governed by geometry, material properties, and resonance modes. Driven by growing demands in high-sensitivity sensing, terahertz communication, and functional imaging, the design space of these devices has rapidly expanded, rendering conventional empirical and parameter-sweeping approaches inefficient for global optimization. Algorithm-driven strategies, particularly those leveraging machine learning and deep learning, have emerged as powerful surrogates for electromagnetic simulation, enabling automated multi-objective optimization and rapid inverse design. Beyond predictive capabilities, these algorithms uncover latent physical mechanisms and support adaptive, closed-loop experimental implementations. Within a unified framework, this review systematically categorizes and summarizes advances in algorithm-driven design of terahertz metamaterials, including traditional optimization methods, machine learning, deep learning, and reinforcement learning, highlighting their roles in multi-layer structural design, multimodal coupling control, and dynamic multi-target detection. Finally, we conduct a detailed analysis of current challenges, including data quality, model generalization, and physical interpretability, and clearly identify future research directions. These directions are expected to lead to practical applications in high-sensitivity chemical and biological detection, terahertz wireless communication, and wave-based functional imaging.

Key words: terahertz metamaterials, AI algorithms, intelligent design, applications

中图分类号:  (Infrared, submillimeter wave, microwave and radiowave instruments and equipment)

  • 07.57.-c
81.05.Xj (Metamaterials for chiral, bianisotropic and other complex media) 07.57.Pt (Submillimeter wave, microwave and radiowave spectrometers; magnetic resonance spectrometers, auxiliary equipment, and techniques) 02.70.-c (Computational techniques; simulations) 41.20.Jb (Electromagnetic wave propagation; radiowave propagation)