Inverse design of broadband metasurface absorber based on convolutional autoencoder network and inverse design network

Abstract

Electromagnetic (EM) metasurfaces have attracted great attention from both engineers and researchers due to their unique physical responses. With the rapid development of complex metasurfaces, the design and optimization processes have also become extremely time-consuming and computational resource-consuming. Here we proposed a deep learning model (DLM) based on a convolutional autoencoder network and inverse design network, which can help to establish the complex relationships between the geometries of metasurfaces and their EM responses. As a typical example, a metasurface absorber consisting of polymethacrylimide foam/metal ring alternating multilayers is chosen to demonstrate the capability of the DLM. The relative spectral error of the two desired spectra is only 5.80 and 5.49, respectively. Our model shows great predictive power and may be used as an effective tool to accelerate the design and optimization of metasurfaces.