Machine Learning for the Design of a Distribution Network for High-Speed Signals

This paper provides a quick overview on three machine learning regression techniques for the uncertainty quantification and the parametric modeling of the responses of electronic systems. Specifically, in this work support vector machine, least-squares support vector machine and Gaussian process regressions are adopted to build accurate and fast-to-evaluate metamodels for the prediction of the behaviour of the output of interest in stochastic systems as a function of the uncertain parameters. The above regressions techniques are trained from a limited set of training pairs provided by either measurements or simulations of the full-computational model. The resulting metamodels can be suitably adopted for both uncertainty quantification and optimization purposes, thus providing the user with a set of helpful tools for the design of complex electrical systems. The feasibility and the accuracy of the considered machine learning regression techniques are investigated by considering a realistic printed circuit board interconnect structure.