Anomaly Detection with Decision Trees for AI Assisted Evaluation of Signal Integrity on PCB Transmission Lines

Abstract

Abstract. Printed circuit board (PCB) design can be supported to a high degree by adding AI modules to the design system. Predictions from these modules can be made available to the designer in order to speed up circuit design and make it more efficient. Problems regarding signal integrity (SI) can be detected in time by providing hints on component connection or routing. However, the optimization and ML methods used in this context are usually very sophisticated (e.g., Bayesian optimization). Therefore, the design parameters provided by the AI modules must be accepted without further insights (for the experienced as well as the inexperienced designer). In this paper, a decision tree for anomaly detection and SI verification is presented, which by nature of this algorithm provides insights to the decisions made to obtain the proposed design parameters. Using a point-to-point (P2P) network as an example, the prediction accuracy of the AI model is investigated. It is shown that assessing SI effects with a decision tree provides a simple approach to obtain the suggested design. Furthermore, the predictions of the decision tree can be verified against the design rules.