A Practical Approach Based on Machine Learning to Support Signal Integrity Design

Abstract

A PCB design system enhanced with AI/ML modules can support the optimal use of microelectronic components in the development process. To do this, the PCB and circuit designer must be provided with AI-based suggestions for SI-compliant interconnection of components in the early design phase. AI-based modules can also serve as a reference for engineers working in the selection of interconnect structures in the pre-, concurrent-, and post-layout analysis phases but having little or no experience with signal integrity (SI). This paper shows from a practical point of view how to create ML modules for SI analysis. Selected ML modules (k-Nearest Neighbor (kNN) + Neural Network (NN - Keras) + Support Vector Regression (SVR)) for predicting design relevant SI parameters for PCB subnetworks are presented.