为应对AI算力芯片TPU PCIe高速接口在多维应力下性能裕度快速缩小的挑战,构建了包含信道损耗、抖动、电源噪声、信道损伤的四维应力模型,开发了自动化测试平台,并引入实验设计(DoE)与方差分析方法,实现了系统级裕度表征与性能边界识别。对某TPU PCIe Gen5链路进行实测与分析,结果表明,信道损耗是影响眼图闭合的首要短板(贡献率为42%),损耗与抖动的交互效应贡献率达18.7%,揭示了高损耗下抖动敏感度显著增强的脆弱点。基于全因子实验绘制的BER=1E-12性能边界图,成功预测了传统单变量测试未能发现的3处失效区域。设计方法实现了从“通过/失败”到“裕度量化”的范式转变,为AI算力芯片高速接口的可靠性设计提供了量化依据。

To address shrinking performance margins in high-speed PCIe interfaces for AI computing chips, this study uses a TPU PCIe Gen5 link as a representative case to establish a four-dimensional stress model encompassing channel loss, jitter, power-supply noise, and transmit equalization. An automated test platform is developed, and Design of Experiments (DoE) together with Analysis of Variance (ANOVA) is applied to enable systematic margin characterization and performance boundary identification. Through measurement and analysis on a TPU PCIe Gen5 link, the results demonstrate that channel loss is the primary bottleneck affecting eye closure, with a contribution of 42%. The loss-jitter interaction contributes 18.7%, revealing the critical vulnerability of significantly amplified jitter sensitivity under high-loss conditions. The BER=1E-12 performance boundary map, derived from the full-factorial experiment, identifies three failure regions that remain undetected by conventional single-variable testing. This methodology achieves a paradigm shift from binary "pass/fail" assessment to quantitative margin analysis, providing a quantitative foundation for the robust design and reliability assessment of high-speed interfaces in AI computing chips.