通过潜在损失分析定位晶圆缺陷源头，提升制造流程诊断精度。
Cross-Process Defect Attribution using Potential Loss Analysis
- 基于部分工艺路径的最优结果对比，推断缺陷源头
- 可同时预测缺陷密度并分配缺陷责任分数
- 适用于复杂多工序半导体制造的缺陷溯源
由于工艺路径中各环节的异质性和组合复杂性，跨工艺的晶圆缺陷根因分析是半导体制造中最关键也最具挑战性的任务之一。本文提出一种新框架——潜在损失分析（Potential Loss Analysis, PLA），作为此前提出的局部轨迹回归方法的重要改进。PLA通过比较部分工艺路径生成的最佳可能结果，将观测到的高缺陷密度归因于上游工艺。我们证明，识别最佳可能结果的问题可转化为求解贝尔曼方程。值得注意的是，该框架能同时解决缺陷密度预测与缺陷评分归因问题。实验基于真实晶圆历史数据验证了该方法的有效性。
原文摘要 · Abstract (English)
Cross-process root-cause analysis of wafer defects is among the most critical yet challenging tasks in semiconductor manufacturing due to the heterogeneity and combinatorial nature of processes along the processing route. This paper presents a new framework for wafer defect root cause analysis, called Potential Loss Analysis (PLA), as a significant enhancement of the previously proposed partial trajectory regression approach. The PLA framework attributes observed high wafer defect densities to upstream processes by comparing the best possible outcomes generated by partial processing trajectories. We show that the task of identifying the best possible outcome can be reduced to solving a Bellman equation. Remarkably, the proposed framework can simultaneously solve the prediction problem for defect density as well as the attribution problem for defect scores. We demonstrate the effectiveness of the proposed framework using real wafer history data.
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