Intelligent Inverse Designs of Impedance Matching Circuits With Generative Adversarial Network

Impedance matching circuits (IMCs) are crucial modules in radio frequency (RF) front-end components, devices, and systems, affecting the performance of the whole systems. However, the design process of IMCs has to require intense manual interventions with high computational costs. To alleviate this problem, a novel scheme for inversely designing IMCs is presented in this work based on neural network technology. Such IMC inverse design framework consists of two mapping-based deep neural networks (DNNs). The first one is an untrained generative adversarial network (GAN) that maps from the design requirements to the regularized S-parameters curves. The second one is an inversion network that maps from the S-parameters and the target impedance to the designed circuit parameters. With the cascaded GAN and inversion network, an efficient method for designing IMC-based filtering antenna is introduced, which takes about 1/17 the time compared to the traditional EM-based design and optimization methods. Further, three power amplifiers (PAs) with multiple IMCs are inversely-designed based on the proposed framework. In experimental demonstration, the elaborate prototypes are fabricated and measured, where the measured results fully satisfy the demand performance.