Yield Multi-Corner Analysis validates circuits across 25+ Process-Voltage-Temperature corners, resulting in a combinatorial simulation cost of $O(K \times N)$ where $K$ denotes corners and $N$ exceeds $10^4$ samples per corner. Existing methods face a fundamental trade-off: simple models achieve automation but fail on nonlinear circuits, while advanced AI models capture complex behaviors but require hours of hyperparameter tuning per design iteration, forming the Tuning Barrier. We break this barrier by replacing engineered priors (i.e., model specifications) with learned priors from a foundation model pre-trained on millions of regression tasks. This model performs in-context learning, instantly adapting to each circuit without tuning or retraining. Its attention mechanism automatically transfers knowledge across corners by identifying shared circuit physics between operating conditions. Combined with an automated feature selector (1152D to 48D), our method matches state-of-the-art accuracy (mean MREs as low as 0.11\%) with zero tuning, reducing total validation cost by over $10\times$.

翻译：良率多角点分析需在超过25个工艺-电压-温度角点下验证电路性能，导致组合仿真成本达到$O(K \times N)$，其中$K$表示角点数量，$N$为每角点超过$10^4$的样本量。现有方法面临根本性权衡：简单模型虽易实现自动化，却无法处理非线性电路；而先进AI模型虽能捕捉复杂行为，但每个设计迭代需耗费数小时进行超参数调优，形成"调优瓶颈"。我们通过用基础模型从数百万回归任务中预训练得到的学习先验替代工程先验（即模型规范），成功突破该瓶颈。该模型通过上下文学习机制，无需调优或重新训练即可即时适配每个电路。其注意力机制通过识别不同工作条件下共享的电路物理特性，自动实现跨角点知识迁移。结合自动特征选择器（1152维降至48维），本方法在零调优条件下达到最先进精度（平均相对误差低至0.11%），并将总体验证成本降低超过10倍。