On knowledge-based neural networks and neuro-space mapping

Abstract

This article reviews the most significant milestones in CAD methodologies for intelligent EM-based modeling and design optimization using artificial neural networks and space mapping. We consider knowledge-based and automatic neural network model generation based on advanced data sampling algorithms. Computationally efficient neural space mapping methods for highly accurate EM-based modeling, statistical analysis and yield estimation are described. We briefly compare different strategies for developing suitable (input and output) neuro-mappings. Inverse modeling exploiting neural networks is addressed, including neural inverse space mapping optimization. Embedded passives, microstrip filters, active devices and waveguide structures illustrate the techniques.