Smart Modeling of Microwave Devices

TLDR

Modeling and computer-aided design techniques are essential for microwave design, and neural networks are suitable for modeling complex relationships that are difficult to derive analytically or computationally expensive. The authors describe neural network approaches for microwave modeling, including straight, inverse, neuro‑SM, and knowledge‑based subpart models. Neural networks evaluate quickly, are easy to implement in microwave CAD, flexibly adapt to varying dimensionality and nonlinearity, and enable parametric or scalable models for passive and active microwave devices.

Abstract

Modeling and computer-aided design (CAD) techniques are essential for microwave design, especially with the drive towards first-pass design success. We have described neural networks for microwave modeling and design. Neural networks are suitable when modeling a required relationship for which analytical formulas are hard to derive, or for which the computational effort is too high. This relationship can be either of the IO relationship of the overall model (straight neural network model), the output-input relationship (inverse model), a relationship between existing model and desired data (neuro-SM), or relationship of a subpart of the overall model (knowledge based neural network). Neural networks are fast to evaluate, and the neural network formulas are easy to implement into microwave CAD. The simplicity of adding input neurons or hidden neurons makes neural network flexible in handling functions of different dimensions and of different degree of nonlinearity. We have also demonstrated that neural networks are helpful in developing parametric or scalable models for passive and active microwave devices.