Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization

2026-05-18

AI Summary

This work addresses the limited adaptability of traditional signal integrity surrogate models, which rely on fixed buffer parameters and require repeated retraining to accommodate process and operating condition variations. The authors propose the first surrogate model that explicitly embeds dynamic buffer characteristics—such as voltage, frequency, and edge rate—into the input alongside PCB parameters, enabling cross-process generalization without retraining and supporting predictions across multiple technology nodes. A comprehensive evaluation of random forest regression, gradient boosting machines, support vector regression, kernel ridge regression, Gaussian process regression, and neural networks is conducted in a 44-dimensional design space, revealing that neural networks significantly outperform other methods in large-data regimes. When applied to eye diagram template compliance checking, the model achieves an order-of-magnitude speedup over conventional simulation while demonstrating strong engineering practicality and generalization capability.

Abstract

Signal integrity (SI) analysis in printed circuit board (PCB) interconnects faces increasing complexity due to diverse integrated circuit (IC) buffer technologies, varying operating conditions, and manufacturing tolerances. Existing machine learning (ML) surrogate models for predicting SI metrics such as the inner eye contour, eye-height (EH), eye-width (EW), and transient waveform features typically rely on fixed buffer parameters, requiring costly new data generation and retraining cycles for every technology shift. This paper introduces a buffer-parameterized ML surrogate modeling methodology capable of handling cross-technology variations without retraining by treating IC buffer characteristics, e.g., clock frequency, supply voltage, rise/fall times, jitter, and internal resistors and capacitors, as dynamic model inputs alongside PCB parameters. To identify the optimal surrogate architecture for this high-dimensional space, a comprehensive benchmarking study compares tree-based methods (RFR/GBM), kernel methods (SVR/KRR), Gaussian process regression (GPR), and neural networks. The framework is subsequently validated on a complex interconnect with 44 design parameters. Results show that while anisotropic GPR excels in low-data regimes, neural networks heavily outperform other models on large datasets. Finally, the practical value of the ML surrogate models is demonstrated through a cross-technology design space exploration and optimization scenario, showcasing massive computational speedups for eye mask compliance checking compared to simulation.

Problem

• signal integrity
• machine learning surrogate models
• buffer parameterization
• cross-technology variation
• PCB interconnects

Innovation

• buffer-parameterized surrogate modeling
• cross-technology generalization
• signal integrity prediction
• machine learning for PCB design
• eye diagram optimization