Design of high-speed links via a machine learning surrogate model for the inverse problem

This paper presents an alternative approach for the design of high-speed link based on a preliminary version of a surrogate model for the inverse problem. Specifically, given the overall structure of the link, our goal is to build an accurate and fast-to-evaluate model for the estimation of the geometrical parameters of its interconnect starting from the desired eye diagram characteristics. The modeling scheme proposed in this paper relies on a powerful machine learning regression technique such as the least-squares support vector machine (LS-SVM) which is used to provide an accurate relationship among the desired eye features and the geometrical parameters of the link interconnect. The proposed model is built from a set of training samples generated by a parametric simulation of the link through the full-computational model. The feasibility and the accuracy of the proposed modeling scheme are then investigated by comparing its predictions with the corresponding results provided by the full-computational model on 250 unseen samples.