Abstract
A novel approach—integrating a simulated annealing (SA) algorithm with deep learning (DL) acceleration—is presented for the rapid and accurate development of terahertz perfect absorbers through forward prediction and backward design. The forward neural network (FNN) effectively deduces the absorption spectrum based on metasurface geometry, resulting in an 80,000-fold increase in computational speed compared to a full-wave solver. Furthermore, the absorber’s structure can be precisely and promptly derived from the desired response. The incorporation of the SA algorithm significantly enhances design efficiency. We successfully designed low-frequency, high-frequency, and broadband absorbers spanning the 4 to 16 THz range with an error margin below 0.02 and a remarkably short design time of only 10 min. Additionally, the proposed model in this Letter introduces a novel, to our knowledge, method for metasurface design at terahertz frequencies such as the design of metamaterials across optical, thermal, and mechanical domains.
© 2024 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Fan Gao, Zhihao Ou, Chenchen Yang, Jinpeng Yang, Juan Deng, and Bo Yan
Opt. Lett. 49(10) 2693-2696 (2024)
Soumyashree S. Panda, Sumit Choudhary, Siddharth Joshi, Satinder K. Sharma, and Ravi S. Hegde
Opt. Lett. 47(10) 2586-2589 (2022)
Shuyi Wang, Tie Hu, Shichuan Wang, Yunxuan Wei, Zihan Mei, Bing Yan, Wenhong Zhou, Zhenyu Yang, JinKun Zheng, YuanLong Peng, and Ming Zhao
Opt. Lett. 49(6) 1595-1598 (2024)