Deep-learning-based neural network for design of a dual-band coupled-line trans-directional coupler

A deep-learning-based model to automate the design of a 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 are generated using the HFSS electromagnetic simulation tool by varying the geometrical design parameters of the coupler. 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, a 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 port phase difference are in good agreement with the ideal values compared with those of the RBFNN, with greater accuracy and speed. The designed coupler was fabricated and measured for verification.