Inverse Design of Few-Layer Metasurfaces Empowered by the Matrix Theory of Multilayer Optics

Few-layer metasurfaces, which are planar artificial arrays composed of more than one functional layer, have been showing unprecedented capabilities for the implementation of integrated and miniaturized optical devices with high efficiency and broad working bandwidth. However, the rich design freedoms of few-layer metasurfaces severely challenge their design and optimization. A universal strategy for the design of few-layer metasurfaces with different desired optical functionalities and an arbitrary number of layers, which can lower the design complexity and the time cost for structural optimization, is still eagerly anticipated by the scientific community. Here, we demonstrate an inverse design strategy based on deep-learning technology for the design of few-layer metasurfaces. By combining the matrix theory of multilayer optics, the proposed algorithm can predict the entire scattering matrix of a few-layer metasurface in tens of seconds with an acceptable accuracy and realize the inverse design of few-layer metasurfaces with different desired functionalities. Thus, the proposed inverse design strategy provides an efficient solution for the reduction of the design complexity of few-layer metasurfaces and significantly lowers the time cost for the structural optimization when compared with the numerical simulation methods based on an iterative process of trial and error, which will be of benefit to and expand the related research.