Dual-Band FSS Inverse Design Using ANN with Cognition-Driven Sampling

Abstract

Recently, artificial neural network (ANN) attracts intensive attentions on solving electromagnetic (EM) inverse problems. In an inverse design of frequency selective surface (FSS) model with ANN, the inputs are S-parameters, while the outputs are structure parameters or material parameters. However, faced with applications where S-parameters vary in a large frequency range with different curve shapes, such as multi-band microwave devices, simple sampling with equal spacing may cause the input dimension to be too large and will require more complex neural network. In this paper, a cognition-driven sampling method is introduced to solve this problem. A parameter-extraction modeling of dual-passband FSS using both equidistant sampling and proposed method is presented and the well-designed FSS is further fabricated to validate the technique.