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 paper 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 paper are generic and thus can be easily extended to the design of any symmetrical planar microwave passive components.

A High-Performance Transfer Learning-Based Model for Microwave Structure Behavior Prediction

“…The convolutional operations with kernels provide translation-equivariant responses known as feature maps. CNN is apt for microwave structure simulation since the position features are vital to predict behavior [15]. However, the structure parameters must be homogeneous when applying CNN.…”

“…Additionally, the neural network simulator can assist in training neural network models for microwave design tasks [13]- [15]. This could help with mitigating the non-uniqueness design problem in microwave design [16].…”