影响因子：2.5

DOI码：10.1016/j.optcom.2025.131958

所属单位：西北大学电子信息学院

教研室：电子科学与技术系

发表刊物：Optics Communications

刊物所在地：Netherlands

项目来源：国际科技合作项目

关键字：Behavior regulation; Comprehensive optimization; Electric field loss;Machine learning (ML); Metamaterial absorber; Multiple narrowband absorption

摘要：Graphene, with its tunable properties and high optical response characteristics, has a wide range of applications in the development of metamaterial absorbers: it is frequently employed as a substitute for the periodic metal structure of conventional metamaterial devices, or utilized as an intermediate layer to composite with the devices. However, precise control over absorption performance within specific narrowbands and the optimization of multi-peak absorption intensity remain key challenges. In this study, a machine learning approach is integrated into the device design process to develop a multilayer heterogeneous composite metamaterial absorber based on graphene, effectively addressing these issues. By combining a patterned metal layer, a graphene thin film layer, and a metal enhancement layer with two dielectric layers and a metallic ground plane, multi-narrowband resonance is successfully achieved. Firstly, machine learning supervision is utilized to effectively control the device's multi-narrowband absorption behaviors within the 0.5–1.2 THz frequency range, leading to the realization of double-peak, triple-peak, and quadruple-peak multi-narrowband absorption behaviors, respectively. Then, leveraging the predictive capabilities of the machine learning model, the structural parameters of the device are tuned to achieve comprehensive optimization of its multi-narrowband absorption performance. As a result, the absorption performance across multiple frequency ranges exceeds 90 %. This method avoids the traditional trial-and-error optimization and provides a scalable design framework for customized multi-narrow band high-performance terahertz absorber.

论文类型：期刊论文

学科门类：工学

一级学科：电子科学与技术

文献类型：J

期号：587

页面范围：131958

是否译文：否

发表时间：2024-12-14

收录刊物：SCI

发布期刊链接：https://www.sciencedirect.com/science/article/abs/pii/S0030401825004869?via%3Dihub

第一作者：Jiaxuan Xue

通讯作者：Cheng Chen*

合写作者：Shilei Tian

合写作者：Huiyao Zhang

合写作者：Jixin Wang

合写作者：Wu Zhao

合写作者：Zhiyong Zhang

合写作者：Johan Stiens