Inverse Design of Dual‐Band Optically Transparent Metasurface Absorbers with Neural‐Adjoint Method

Abstract

AbstractMassive applications of microwave technology have led to a large amount of electromagnetic pollution, which seriously interferes with the normal operation of communication systems in people's daily life. Optically transparent metasurface absorber is emerging as a promising candidate for solving this problem in some specific scenarios. When designing multiband metasurface absorbers, the coupling within the cell structure generally prevents the independent modulation of the absorption effect in each band. The rapid development of deep learning provides a new way for designing high‐performance metasurface. Here, a forward network model is built to predict the absorption spectrum of the metasurface absorber. In the network model, 1D (one‐dimensional) inverse convolution is used as the upsampling layer, which enables the network model to have a good prediction on small data sets while avoiding overfitting on large data sets. Based on the network model, a dual‐band optically transparent metasurface absorber is also designed by employing the neural‐adjoint (NA) method. The results taking advantage of deep learning method for high‐efficiency design of metasurface absorbers without considering the coupling effect, have shown great capability to optimize the absorption efficiency of different frequency bands independently, which may provide an alternative way to design other multiband microwave devices.