Multi-scale inverse design of optical metasurfaces using physics-informed computational intelligence

Interest in inverse design for the efficient and accurate design of optical devices has increased in recent years. In the case of complex optical problems which span several orders of magnitude, inverse design is an especially difficult problem. In this paper we propose a multi-scale inverse design process which leverages machine learning tools to encode the numerical simulation of optical wave propagation and material wave modulation directly as layers of a neural network. This requires consideration of both the near field electromagnetic response with respect to metasurface (material) devices, as well as far field effects as the wave propagates through space. The end result is the efficient modeling and optimization spanning several orders of magnitude.