PCB信号完整性分析团队终于有了能跨技术代际复用的ML代理模型，不用每次换缓冲器参数就重新生成数据和训练，做高速互连设计的工程师可以直接用这套框架加速眼图合规检查。
论文提出一种缓冲器参数化的机器学习代理模型方法，将IC缓冲器特性（如时钟频率、电源电压、上升/下降时间、抖动及内部电阻电容）作为动态输入，与PCB参数共同建模，从而无需重新训练即可处理跨技术变化。通过对比树模型、核方法、高斯过程回归和神经网络等架构，发现各向异性高斯过程回归在小数据场景表现优异，而神经网络在大数据集上显著领先。该方法在44个设计参数的复杂互连上验证，展示了相比传统仿真在眼图合规检查中巨大的计算加速。
Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization
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.