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This research advances machine learning applications in hardware design by developing surrogate models that generalize across diverse IC buffer technologies without requiring retraining. By treating buffer characteristics as dynamic inputs, the study enables significant computational speedups for signal integrity optimization and eye mask compliance checking in complex PCB interconnects. The comparative analysis of various ML architectures highlights neural networks' superiority on large datasets while identifying Gaussian process regression as effective for low-data regimes.
Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization
arXiv:2605.18170v2 Announce Type: replace-cross 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 the prediction of SI metrics such as the inner eye contour, eye-height (EH), eye-width (EW), and transient waveform features often rely on fixed buffer parameters, limiting the adaptability across design and technology variations. 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 model inputs in addition to the PCB parameters. To identify the optimal surrogate architecture for this high-dimensional space, tree-based methods (RFR/GBM), kernel methods (SVR/KRR), Gaussian process regression (GPR), and neural networks are compared. The buffer-parameterized approach is validated using a complex interconnect with a total of 44 design parameters. In addition to demonstrating strong generalization, the results show that neural networks outperform other ML models on large datasets, while the anisotropic GPR excels in low-data regimes. Finally, the practical value of the ML surrogate models is demonstrated through a cross-technology design space exploration and optimization scenario, which indicates large computational speedups for eye mask compliance checking compared to simulation.
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 the prediction of SI metrics such as the inner eye contour, eye-height (EH), eye-width (EW), and transient waveform features often rely on fixed buffer parameters, limiting the adaptability across design and technology variations. 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 model inputs in addition to the PCB parameters. To identify the optimal surrogate architecture for this high-dimensional space, tree-based methods (RFR/GBM), kernel methods (SVR/KRR), Gaussian process regression (GPR), and neural networks are compared. The buffer-parameterized approach is validated using a complex interconnect with a total of 44 design parameters. In addition to demonstrating strong generalization, the results show that neural networks outperform other ML models on large datasets, while the anisotropic GPR excels in low-data regimes. Finally, the practical value of the ML surrogate models is demonstrated through a cross-technology design space exploration and optimization scenario, which indicates large computational speedups for eye mask compliance checking compared to simulation.
12 pages, 16 figures, 7 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Signal Processing (eess.SP); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
arXiv:2605.18170 [eess.SP]
(or arXiv:2605.18170v2 [eess.SP] for this version)
Full text under the publisher's open licence · 263 words