人工智能在太赫兹超材料设计与优化领域的研究进展  

Advances in Artificial Intelligence for Design and Optimization of Terahertz Metamaterials

作　　者：葛宏义 补雨薇[1,2,3] 蒋玉英 季晓迪 贾柯柯[1,2,3] 吴旭阳 张元 张玉洁 孙庆成 王顺[1,2,3] Ge Hongyi;Bu Yuwei;Jiang Yuying;Ji Xiaodi;Jia Keke;Wu Xuyang;Zhang Yuan;Zhang Yujie;Sun Qingcheng;Wang Shun(Key Laboratory of Grain Information Processing and Control,Ministry of Education,Henan University of Technology,Zhengzhou,450001,Henan,China;Henan Province Key Laboratory of Grain Photoelectric Detection and Control,Zhengzhou 450001,Henan,China;College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,Henan,China;School of Artificial Intelligence and Big Data,Henan University of Technology,Zhengzhou 450001,Henan,China)

机构地区：[1]河南工业大学粮食信息处理与控制教育部重点实验室,郑州450001 [2]河南省粮食光电探测与控制重点实验室,郑州450001 [3]河南工业大学信息科学与工程学院,郑州450001 [4]河南工业大学人工智能与大数据学院,郑州450001

出　　处：《激光与光电子学进展》2024年第23期28-47,共20页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62271191,61975053);河南省自然科学基金(222300420040);河南省重点科技计划项目(222102110246,222103810072);河南省高校科技创新人才支持计划资助(23HASTIT024,22HASTIT017);河南工业大学创新基金(2021ZKCJ04)。

摘　　要：近年来,基于人工智能技术的超材料器件设计方法成为当前研究的焦点。人工智能技术的加入显著减少了超材料器件传统设计方法对设计人员电磁学理论及仿真经验的依赖,并有效缩减研究者的设计周期。然而,尽管该领域取得了一定进展,但太赫兹波段的器件设计仍然是一个相对欠发展的领域。首先从器件功能的角度出发,对太赫兹超材料器件传统设计方法进行了细致梳理,同时关注可调谐多功能超材料器件的研究现状。此外,详细阐述了人工智能技术(机器学习、进化算法、深度学习等)如何为超材料器件基于结构参数的正向优化与基于电磁响应的逆向设计过程提供支持。最后对该领域的未来发展和挑战进行了探讨,旨在为从事相关研究的科研人员提供有益参考。Recently,there has been a growing interest in using artificial intelligence(AI)technology to design metamaterial devices.This approach reduces the reliance on traditional design methods that require expertise in electromagnetics theory and simulation,resulting in a more efficient design cycle.Despite the progress made in this field,device design in the terahertz band remains relatively underdeveloped.This paper scrutinizes traditional design methods of terahertz metamaterial devices from the perspective of device function and focuses on the current research status of tunable multifunctional metamaterial devices.The article discusses how artificial intelligence techniques,such as machine learning,evolutionary algorithms,and deep learning,can aid in the optimization of metamaterial devices based on structural parameters and electromagnetic response.Finally,this section discusses future developments and challenges in the field,providing useful references for researchers engaged in related studies.

关 键 词：太赫兹 超材料 器件优化 人工智能 

分 类 号：TN828.4[电子电信—信息与通信工程] TB381[一般工业技术—材料科学与工程]