Neural Networks for RF and Microwave Design

Chapter 7: Active Component Modeling Using Neural Networks

This chapter introduces applications of neural networks to active component modeling. The description includes direct modeling of the device external behavior using neural networks for DC, small-signal, and large-signal device modeling. It also describes indirect neural modeling through a known equivalent circuit of the device, and its application in global modeling of microwave circuits. Discussions on the incorporation of the large-signal neural model into circuit simulations and time-varying Volterra kernel-based neural models for transistors are provided.

7.1 Introduction

Active device modeling is one of the most important areas of microwave CAD. Most frequently-used approaches in today's circuit design are based on lumped equivalent circuits, an example of which is shown in Figure 7.1. A large variety of such equivalent circuit models have been developed in the past, because no single equivalent model can represent all kinds of transistor behaviors. The specific equivalent circuit structure in a model optimized for one type of device becomes a limitation of the model for other devices. With the rapid technology change, new types of semi-conductor devices are constantly evolving, and development of models to represent the new transistor behaviors is a continuous activity. Developing new equivalent circuit models requires human experience and judgment. Often a time-consuming trial-and-error process is used for formulating new equivalent circuit topology and for creating formulas fornonlinear elements. Recently, researchers have started investigating neural network approaches for representing transistor DC, small-signal, and large-signal behaviors. Neural networks have the potential of modeling new transistor behaviors through computerized training, thus possibly making...

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