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

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-integrated reconfigurable metasurfaces for focused vortex beam generation are inversely designed by counter propagating the DNN with fraction error less than 10−4. The results indicate that transmission-reflection-integrated achromatic performances are well achieved in the frequency range of 0.7–1.3 THz, which have the average focusing efficiency and mode purity higher than 48 % and 92 %, respectively. Moreover, transmission-reflection-integrated achromatic THz imaging and edge detection can also be realized by the metasurfaces. This work provides a high accuracy inverse design method for conceiving multifunctional meta-devices, which may promise further progress for the on-chip THz imaging systems.