A Learning-Based Methodology for Microwave Passive Component Design

Abstract

Microwave passive component design is of particular interest to radio frequency (RF) scholars and engineers. Although a plethora of studies have been carried out over multiple decades, designing high-frequency structures that offer high performance still heavily relies on heuristic methods and even rules of thumb. Thus, the process is often inefficient, and outcomes are not guaranteed. This article proposes a novel cascaded convolutional neural network (CNN) model to speed up the design process of planar microwave passive components. Given target behavior specifications, our proposed neural network model can quickly and accurately suggest proper component structures for single or multiple frequency bands. The feasibility and reliability of our model are validated here by both electromagnetic (EM) simulation and a fully instrumented implementation. The experimental results demonstrate that the proposed model can design planar passive components, including two-port matching networks and three-port power dividers. Moreover, our model provides passive component topologies that are fundamentally different from canonical number-limited templates and, therefore, yields novel architectures for passive microwave components. It also facilitates rapid passive components design flow for targeted electrical behavior within a limited board area. The proposed cascaded CNN model and the associated methodologies in this article are generic and, thus, can be easily extended to the design of any symmetrical planar microwave passive components.