Inverse design of a metasurface based on a deep tandem neural network
Peng Xu, Jun Lou, Chenxia Li, Xufeng Jing
Abstract
Compared with traditional optical devices, metasurfaces have attracted extensive attention due to their unique electromagnetic properties as well as their advantages of thinness, ease of integration, and low loss. However, structural modeling, simulation calculations, and parameter optimization processes are often required for metasurface design by traditional methods, which consume time and computing resources. Here, we propose an inverse design method based on deep tandem neural networks to speed up the design process of metasurfaces. This method connects the pretrained forward prediction model and the inverse design model in series, which effectively solves the problem that the model is difficult to converge due to the nonuniqueness problem. A trained inverse design model can design metasurface structures that conform to a given spectral target in a very short time. Therefore, this paper demonstrates the feasibility of using deep tandem neural networks for metasurface inverse design, which greatly shortens the design time of metasurfaces and provides a reference for researchers to design metamaterial structures with specific optical properties.