Machine learning-optimized terahertz ultra-wideband tunable metamaterial absorber
- 影响因子：5.1
- DOI码：10.1016/j.diamond.2025.112793
- 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：Diamond and Related Materials
- 刊物所在地：Netherladn
- 项目来源：国际科技合作项目
- 关键字：Metamaterial absorber; Ultra-wideband; Terahertz; Graphene; Machine learning; Tunability
- 摘要：Ultra-wideband absorbers are essential devices capable of efficiently absorbing electromagnetic waves over a broad frequency range, with extensive applications in radar detection, wireless communication, and stealth technology. Their primary advantage lies in the ability to simultaneously cover both low and high-frequency absorption bands, thereby significantly enhancing stealth performance and anti-interference capabilities. However, the design of ultra-wideband absorbers still faces two major technical challenges: first, achieving stable absorption performance across an ultra-wide frequency range; and second, further improving absorption efficiency while maintaining broadband stability to meet the demands of various application scenarios. In this study, we propose a terahertz metamaterial absorber based on a three-layer composite structure incorporating patterned graphene sheets. This structure enables dynamic tunability between absorption and reflection states. To optimize the absorption performance, an innovative machine learning-based optimization strategy is introduced. Firstly, forwarding prediction is employed to quantify the optimization weights of different structural parameters, allowing for the selection of key tunable parameters. Subsequently, inverse prediction is utilized to determine the optimal structural configuration based on the target absorption performance. As a result, the proposed design achieves an absorption rate exceeding 90 % within the 2.28–4.68 THz frequency range, demonstrating significant improvements in absorption efficiency and tunability.
- 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
- 卷号：Part B
- 期号：195
- 页面范围：112793
- 是否译文：否
- 发表时间：2025-11-01
- 收录刊物：SCI
- 发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0925963525008507
- 第一作者：Shilei Tian
- 通讯作者：Cheng Chen*
- 合写作者：Jiaxuan Xue
- 合写作者：Zhihao Li
- 合写作者：Jixin Wang
- 合写作者：Johan Stiens
