Multifunctional Metasurface Design via Physics-Simplified Machine Learning

Abstract

Metasurface can manipulate electromagnetic (EM) waves flexibly, which provides the basis for functional integration. Recently, the efficient machine-learning-assisted methods have attracted intensive attentions in multifunctional metasurfaces design. However, the conventional machine-learning-assisted metasurfaces design is to fit the internal relationship in the form of black box, which ignores the underlying physical logic, resulting in the increased complexity of machine learning architecture with the parameters increasing. In order to adapt to the multiparameter optimization in multifunctional metasurfaces design, we propose a multiplexing neural network (MNN) based on decoupling at the physical layer to simplify both the structural parameters and the network architecture. The four interacting parameters are simplified into four independently regulated parameters so that the facile design of four functions can be realized only by multiplexing a simple neural network. For verification, four functions of scattering, anomalous reflection, focusing, and hologram are integrated in the same metasurface aperture by MNN. Performances of the metasurface are fully demonstrated by simulation and measurement. Importantly, this work paves the way for the bidirectional simplification of machine learning and metasurface design via physical inspiration, which provides an integrated design method of multifunctional metasurfaces and can be potentially applied to satellite communications and other fields.