A Machine Learning Modeling and Optimization Framework for Signal Integrity Design Support

In this paper, a general framework is proposed to assist signal integrity (SI) design through machine learning (ML) modeling and optimization. The four main aspects of the framework, namely, the topology library, the PCB & circuit parameters, the ML model zoo, and the optimization, are addressed. Based on two application scenarios, the feasibility and scalability of the proposed approach is demonstrated.