High accuracy inverse design of reconfigurable metasurfaces with transmission-reflection-integrated achromatic functionalities.

ABSTRACT: Artificial intelligence algorithms based on deep neural network (DNN) have become an effective tool for conceiving metasurfaces recently. However, the complex and sharp resonances of metasurfaces will tremendously increase the training difficulty of DNNs with non-negligible prediction errors, which hinders their development in designing multifunctional metasurfaces. To overcome the obstacles, the interaction mechanisms between meta-atoms and terahertz (THz) waves via multipole decomposition are investigated to establish a high-quality dataset, which can decrease the complexity of DNN and improve the prediction accuracy. Meanwhile, transfer learning is also employed to reduce the large quantity of training data required by the DNN. Accordingly, two broadband and transmission-reflection-inte

SUBMITTER: Jiang XQ 

PROVIDER: S-EPMC11980877 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature