Adaptive frequency sampling using linear Bayesian vector fitting

Abstract

The authors present a novel Bayesian approach to adaptively select frequency samples to obtain a rational macromodel of device responses over a broad frequency range while performing as few electromagnetic simulations as possible. The method leverages a Bayesian approach to vector fitting to construct a data-driven uncertainty measure. The presented technique is demonstrated by application to a double semi-circular patch antenna and is shown to accurately and efficiently construct a rational macromodel over the frequency range of interest.