Deep-Learning-Based Neural Network for Design of Dual-Band Coupled-Line Trans-Directional Coupler

Abstract

A deep-learning-based model to automate the design of dual-band coupled-line trans- directional (CL-TRD) coupler can greatly improve upon the current techniques that rely on complicated analysis. In this paper, we propose a convolutional neural network (CNN), which is a type of deep learning, which can rapidly output the parameters of dual-band directional couplers corresponding to theoretical (ideal) specifications of the electrical parameters through an inverse model. The neural network training data is generated by the use of electromagnetic simulation tool HFSS by varying the geometrical design parameters of the coupler. In order to validate the robustness of the CNN inverse model, it is applied on a 3-dB dual-band CL-TRD coupler operating at 1.2/4 GHz, and compared with a shallow neural network namely, radial basis function neural network (RBFNN). The coupler parameters designed by both neural networks are verified by HFSS. The results reveal that the CNN simulated S-parameters and output ports phase difference are in good agreements with the ideal ones compared to those of RBFNN with more accuracy and speed. The designed coupler is fabricated and measured for verification.