EM-Based Optimization of Microwave Circuits Using Artificial Neural Networks: The State-of-the-Art

TLDR

This review surveys EM‑based microwave circuit design and optimization with artificial neural networks, discusses challenges in developing synthesis neural networks, and outlines future research directions. The paper examines measurement‑based design, conventional neural optimization, enhancement techniques such as segmentation and clustering, neural space‑mapping, EM‑based statistical analysis and yield optimization, and the use of ANNs to accelerate global modeling of monolithic microwave integrated circuits. Key issues in transient EM‑based design using neural networks are identified and summarized.

Abstract

This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments, and clusterization. Innovative strategies for ANN-based design exploiting microwave knowledge are reviewed, including neural space-mapping methods. The problem of developing synthesis neural networks is treated. EM-based statistical analysis and yield optimization using neural networks is reviewed. The key issues in transient EM-based design using neural networks are summarized. The use of ANNs to speed up "global modeling" for EM-based design of monolithic microwave integrated circuits is briefly described. Future directions in ANN techniques to microwave design are suggested.