Abstract
In this Letter, a transfer learning method is proposed to complete design tasks on heterogeneous metasurface datasets with distinct functionalities. Through fine-tuning the inverse design network and freezing the parameters of hidden layers, we successfully transfer the metasurface inverse design knowledge from the electromagnetic-induced transparency (EIT) domain to the three target domains of EIT (different design), absorption, and phase-controlled metasurface. Remarkably, in comparison to the source domain dataset, a minimum of only 700 target domain samples is required to complete the training process. This work presents a significant solution to lower the data threshold for the inverse design process and provides the possibility of knowledge transfer between different domain metasurface datasets.
© 2024 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Alejandro Velez-Zea, Cristian David Gutierrez-Cespedes, and John Fredy Barrera-Ramírez
Opt. Lett. 49(3) 514-517 (2024)
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)
Soumyashree S. Panda, Sumit Choudhary, Siddharth Joshi, Satinder K. Sharma, and Ravi S. Hegde
Opt. Lett. 47(10) 2586-2589 (2022)