太赫兹科学与电子信息学报2026，Vol.24Issue(5)：561-573,13.DOI:10.11805/TKYDA2025038
基于机器学习赋能的太赫兹超表面设计与应用
Machine learning-enabled terahertz metasurface design and applications
黄欣宇 1陈本纹 1吴敬波 1范克彬 1张彩虹 1金飚兵 1陈健 1吴培亨1
作者信息
- 1. 南京大学 电子科学与工程学院,江苏 南京 210023
- 折叠
摘要
Abstract
In recent years,the integration of terahertz metasurfaces with machine learning technologies has injected new vitality into this field.Leveraging efficient iterative rates and diverse network architectures,machine learning has been widely employed on one hand as surrogate models for full-wave numerical simulations and intelligent design of terahertz metasurfaces;on the other hand,terahertz metasurfaces provide a computing platform that operates at the speed of light for optical computing.This paper focuses on the application of machine learning in terahertz metasurfaces.It first elaborates on the design frameworks and principles based on deep neural networks and generative adversarial networks,revealing multi-dimensional parameter optimization mechanisms.Subsequently,taking the all-optical diffractive deep neural network as an example,it introduces the physical mechanisms by which metasurfaces implement machine learning algorithms and their derived applications.Finally,it prospects the development trends and application prospects of the integration between machine learning and terahertz metasurfaces.关键词
太赫兹/超表面/机器学习/神经网络/逆向设计/衍射深度神经网络Key words
terahertz/metasurfaces/machine-learning/neural networks/inverse-design/diffractive deep neural network分类
信息技术与安全科学引用本文复制引用
黄欣宇,陈本纹,吴敬波,范克彬,张彩虹,金飚兵,陈健,吴培亨..基于机器学习赋能的太赫兹超表面设计与应用[J].太赫兹科学与电子信息学报,2026,24(5):561-573,13.
