零调参实现多工况电路验证，精度媲美顶尖方法
Breaking the Tuning Barrier: Zero-Hyperparameters Yield Multi-Corner Analysis Via Learned Priors
- 用预训练基础模型学习先验，无需调参即可适配新电路
- 25+工况下验证误差低至0.11%，总成本降低超10倍
- 适合需要快速迭代的芯片设计团队，尤其擅长非线性电路
多工况电路验证需覆盖25个以上工艺-电压-温度组合，导致组合仿真开销达$O(K \times N)$，其中$K$为工况数，$N$每工况超$10^4$样本。现有方法存在根本矛盾：简单模型虽可自动运行但无法处理非线性电路，而先进AI模型虽能捕捉复杂行为，却需每轮设计迭代耗时数小时调参，形成“调参壁垒”。本文通过将人工设计的先验替换为在百万回归任务上预训练的基础模型所学习的先验，实现上下文学习，无需调参或重训练即可快速适应每个电路。其注意力机制自动识别不同工况间的共享电路物理规律，实现知识迁移。结合自动特征选择器（1152维降至48维），方法达到业界顶尖精度（平均相对误差低至0.11%），且全程零调参，总验证成本降低超过10倍。
原文摘要 · Abstract (English)
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 $10\times$.
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