Probabilistic Model of the Effect of a Ground Discontinuity on the Transmission of a Microstrip Interconnect

Abstract

This paper presents a probabilistic model for the prediction of the transmission performance of a miscrostrip link in presence of a ground discontinuity. The proposed model is based on a machine learning approach. Specifically, it combines the least-squares support vector machine regression with a Gaussian process regression with the aim of predicting the first resonance frequency of a miscrostrip structure for different geometries and positions of a rectangular slit in the ground plane. The model is trained from a small set of costly electromagnetic simulations generated via a latin hypercube sampling scheme and provides also the confidence intervals of its predictions. The accuracy and the capability of the proposed modeling approach are demonstrated by comparing the model predictions and their relative confidence intervals with the results provided by a parametric full-wave electromagnetic simulation.