Deep Neural Network with Data Cropping Algorithm for Absorptive Frequency‐Selective Transmission Metasurface

Abstract Deep neural networks (DNNs) are widely used in designing a metasurface; however, data acquisition from simulations is expensive in terms of time and effort. Inspired by image cropping, a data cropping algorithm is proposed that can significantly reduce the simulation time required in the DNN pre‐training process. The algorithm crops the simulated data and adds random data to augment the amount of the dataset. By applying the proposed target‐driven DNN, an absorptive frequency‐selective transmission (AFST) metasurface structure with a low profile and a broad transmission band is designed. A transmission band from 7.5 to 14 GHz and an absorption rate as large as 0.75 beyond the transmission band are observed. The proposed method provides an efficient strategy to design metasurfaces and a fast solution to the electromagnetic inverse design problem.