Ultrawideband Schiffman Phase Shifter Designed With Deep Neural Networks

Abstract

This article presents a novel method for the forward modeling and inverse design of a class of Schiffman phase shifters using deep neural networks (DNNs). Since DNNs are capable of mapping the highly nonlinear correlations between inputs and outputs, we constructed a fully connected DNN to predict the electromagnetic (EM) responses of Schiffman phase shifters given their physical dimensions. Based on this fast and accurate modeling tool, a cascaded inverse design DNN was then built and trained to achieve instant on-demand phase shifter designs. This approach is versatile and can be easily modified to accomplish different design goals. To demonstrate its efficacy, we trained two DNNs to realize Schiffman phase shifter designs with different bandwidths (40% and 60%) and arbitrary phase shift targets (0°–180°). Simulation results and experimental verifications substantiate that their performances are comparable with the state-of-the-art designs. Moreover, we discussed the proposed methods’ potential in dealing with design tasks that are nonintuitive and beyond the scope of the existing approaches. We envision that this DNN approach can be extended to the design of various EM components including but not limited to antennas, filters, power dividers, and frequency selective surfaces (FSS).