Artificial neural networks for rf and microwave design-from theory to practice

TLDR

Neural networks are increasingly used for RF and microwave modeling and design, learning component behavior and offering fast, accurate alternatives to expensive numerical, difficult analytical, or limited empirical methods. This tutorial teaches RF/microwave engineers the fundamentals of neural networks, their usefulness, appropriate application scenarios, and practical usage. It details neural‑network architectures and training techniques from a designer’s view, including electromagnetics‑based training for passive components, physics‑based training for active devices, and applications to circuit design and yield optimization. The electronic version includes a multimedia slide deck with audio and a link to the NeuroModeler demonstration software for hands‑on practice.

Abstract

Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. A trained neural network can be used for high-level design, providing fast and accurate answers to the task it has learned. Neural networks are attractive alternatives to conventional methods such as numerical modeling methods, which could be computationally expensive, or analytical methods which could be difficult to obtain for new devices, or empirical modeling solutions whose range and accuracy may be limited. This tutorial describes fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them. Neural-network structures and their training methods are described from the RF/microwave designer's perspective. Electromagnetics-based training for passive component models and physics-based training for active device models are illustrated. Circuit design and yield optimization using passive/active neural models are also presented. A multimedia slide presentation along with narrative audio clips is included in the electronic version of this paper. A hyperlink to the NeuroModeler demonstration software is provided to allow readers practice neural-network-based design concepts.