Inverse Design of Dual-Band Microstrip Filters Based on Generative Adversarial Network

Conventional design approaches for microstrip filters involve complex mathematical computations and exhaustive parameter tuning, which require a substantial investment of time and intellectual resources. In this letter, we present an inverse design model based on conditional deep convolutional (CDC) generative adversarial network (GAN) to significantly simplify the design process of dual-band microstrip filters. The circuit structure of the filters consists of two fixed feedlines and a square patch with irregular notches that is formed by 32 $\times$ 32 pixels. By establishing the relationship between the pixelated patterns and their corresponding ${S}$ -parameters, the inverse design problem of the filters is converted and simplified to the inverse design problem of the pixelated patterns. It is addressed by an inverse design model based on GAN that is constructed with three convolutional neural networks (CNNs). When feeding a set of customized ${S}$ -parameters into the inverse design model, a series of special pixelated patterns are generated with the assistance of the GAN in about 11 min. Four design examples of dual-band filters with center frequencies located in ${S}$ / ${C}$ -band and ${C}$ / $L$ -band, respectively, are provided to validate the effectiveness of the inverse design model. The simulated ${S}$ -parameters of the inversely designed filters are in good agreement with the customized ones. Two practical examples of dual-band microstrip filters operating at 3 and 5 GHz are presented to further demonstrate the feasibility of the proposed inverse design method.