Application of Different Learning Methods for the Modelling of Microstrip Characteristics

Abstract

In this paper, the performance of four machine learning regressions like Support Vector Machine (SVM), Least Square-Support Vector Machine (LS-SVM), Gaussian Process Regression (GPR) and Random Forest method (RF) are investigated by means of an illustrative example referring to the characteristic impedance of a microstrip line in terms of electrical and geometrical parameters. The required dataset for training is obtained from a set of parametric electromagnetic simulations. The performance comparison of the four methods is done in the presence and absence of numerical noise and inaccuracies affecting the training samples. The results of our comparison provide a guidance for the proper method selection to model the electromagnetic characteristics of interconnects for high-speed signals: advantages and drawbacks of each of the proposed techniques clearly emerge from this analysis.