Physics-Driven Machine-Learning Approach Incorporating Temporal Coupled Mode Theory for Intelligent Design of Metasurfaces

Metasurfaces find a wide variety of applications in the last decades due to their powerful ability to manipulate electromagnetic (EM) waves. Traditional approaches for metasurface design require massive full-wave EM simulations to achieve optimal geometrical parameter values, resulting in an inefficient design process of metasurfaces. In this article, we propose a physics-driven machine-learning (ML) approach incorporating temporal coupled mode theory (CMT) to improve the design efficiency and implement an intelligent design of metasurfaces. In the proposed approach, a surrogate model (i.e., neuro-CMT model) is developed to speed up the prediction of EM responses of metasurfaces. A three-stage method is used to develop the neuro-CMT model. First, we perform full-wave EM simulations of unit cells only containing single- and double-resonators for different geometrical design parameter values. Second, we extract the single- and double-resonator CMT parameters for each geometrical parameter value by fitting the corresponding EM responses based on CMT equations. Third, we train neural networks to learn the relationships between the CMT parameters and geometrical parameters for single- and double-resonator systems, respectively. These trained neural networks, in conjunction with the multiresonator CMT equation, become an efficient tool to accurately predict the EM responses of any arbitrary coupled multiresonator systems. The proposed neuro-CMT model can be further utilized for metasurface design optimizations. Two metasurface absorbers are given as examples to demonstrate the efficient and intelligent advantages of our proposed approach.