Surrogate-Assisted Framework for SI-Compliant Interconnect Design Optimization Using the Earth Mover's Distance

2026-06-13

AI Summary

This work addresses the limitations of traditional PCB signal integrity (SI) design methodologies, which rely on opaque, iterative black-box approaches lacking interpretability and determinism. To overcome these issues, the authors propose a deterministic optimization framework that integrates a neural surrogate model, a physics-informed decision tree classifier, and Earth Mover’s Distance (EMD). The neural network predicts waveform characteristics, while the decision tree enforces SI compliance based on physical principles. EMD quantifies the similarity between candidate designs and an ideal reference signal, enabling transparent identification and ranking of high-performance configurations without inverse modeling or stochastic search. Experimental validation on a large-scale DDR3 fly-by simulation dataset demonstrates that the framework efficiently locates SI-compliant parameter regions and yields design rankings with clear physical interpretability.

Abstract

This work presents a deterministic, machine-assisted framework for SI-compliant PCB design based on the Earth Mover's Distance (EMD). In contrast to conventional surrogate-based optimization methods that rely on iterative black-box search procedures, the proposed approach follows an interpretable, sequential evaluation strategy. Neural surrogate models are first used to efficiently predict waveform describing features from topology-dependent design parameters. A decision tree then acts as a physically motivated quality gate that identifies SI-compliant waveforms according to predefined SI criteria. Within the resulting valid solution space, the Earth Mover's Distance is employed as a similarity metric to rank candidate designs according to their proximity to an ideal reference signal. This enables not only the deterministic identification of admissible parameter regions but also a transparent prioritization of physically superior solutions without inverse modeling or stochastic search procedures. The methodology is demonstrated using a large-scale set of simulated DDR3 fly-by waveforms. By combining surrogate prediction, interpretable classification, and EMD-based waveform evaluation, the framework provides an explainable and computationally efficient alternative to conventional optimization strategies for supporting PCB development with AI-based methods.

Problem

• Signal Integrity
• Interconnect Design
• Surrogate Modeling
• Earth Mover's Distance
• PCB Optimization

Innovation

• Surrogate-assisted optimization
• Earth Mover's Distance
• Signal Integrity
• Interpretable AI
• PCB design