Low‐Cost Surrogate Modeling for Expedited Data Acquisition of Reconfigurable Metasurfaces

Abstract

In recent years, machine learning (ML) and deep learning (DL) have been widely used to break the metasurface’s performance ceiling. However, the existing data‐driven ML and DL methods usually require the availability of vast amounts of training data to ensure their stable and accurate performance. The process of acquiring these data is high‐cost due to the need for numerous full‐wave electromagnetic (EM) simulations. Here, we propose a low‐cost surrogate model to generate these data efficiently. The proposed model employs microwave network theory to separate meta‐elements into four independent components. Through integration with transmission line theory, we derive the EM responses of meta‐elements using analytical representation with the active device equivalent impedance and dielectric as design variables. Two typical phase‐modulation active meta‐elements are employed to verify the accuracy of our macromodel in comparison with full‐wave EM simulations. Based on the developed macromodel, the superior prediction ability is further presented to illustrate the performance of meta‐elements with various active devices and dielectric substrates. The proposed macromodel is a feasible and general method to rapidly obtain the necessary training data of active meta‐elements, which holds a great potential to significantly reduce the designing time of ML and DL models for the active metasurfaces.