Deep learning based inverse design of multi-functional metasurface absorbers

• Yikun Huang, Xiaoshan Liu, Mulin Liu, Jing Chen, Wei Du, and Zhengqi Liu
• received 01/12/2024; accepted 04/18/2024; posted 04/18/2024; Doc. ID 518786
• 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 SA algorithm significantly enhances design efficiency. We successfully designed low-frequency, high-frequency, and broadband absorbers spanning the 4 THz to 16 THz range with an error margin below 0.02 and a remarkably short design time of only 10 minutes. The proposed model in this letter introduces a novel method for metasurface design at terahertz frequencies.