Dynamic Attention Mixer-Based Residual Network Assisted Design of Holographic Metasurface

Multi-channel holographic metasurfaces have great potential for applications in wireless communications and radar. However, geometric phase-based multichannel metasurface units often have complex phase spectra, making the design of holographic metasurfaces complex and time-consuming. To address this challenge, we propose a dynamic attention mixer-based residual network to streamline the optimization and design of a multi-channel holographic metasurface unit. We conduct validation using multi-channel metasurface units, with a training set mean squared error (MSE) of 0.003 and a validation set MSE of 0.4. Additionally, we calculate the mean absolute error (MAE) for the geometric parameters θ1 and θ2 of the backward-predicted metasurface units in the validation set, which are 0.2° and 0.6°, respectively. Compared to traditional networks, our method achieves robust learning outcomes without the need for extensive datasets and provides accurate results even in complex electromagnetic responses. It is believed that the method presented in this paper is also applicable to the design of other artificial materials or multifunctional metasurfaces.