Deep-learning-enabled electromagnetic near-field prediction and inverse design of metasurfaces

Abstract

Metasurfaces generate desired electromagnetic wavefronts using sub-wavelength structures that are much thinner than conventional optical tools. However, their typical design method is based on trial and error, which is adversely inefficient in terms of the consumed time and computational power. This paper proposes and demonstrates deep-learning-enabled rapid prediction of the full electromagnetic near-field response and inverse prediction of the metasurfaces from desired wavefronts to obtain direct and rapid designs. The proposed encoder–decoder neural network was tested for different metasurface design configurations. This approach overcomes the common issue of predicting only the transmission spectra, a critical limitation of the previous reports of deep-learning-based solutions. Our deep-learning-empowered near-field model can conveniently be used as a rapid simulation tool for metasurface analyses as well as for their direct rapid design.