用序列感知方法定位晶圆缺陷的上游问题，提升半导体制造诊断效率
Sequence-Aware Inline Measurement Attribution for Good-Bad Wafer Diagnosis
- 基于序列敏感的谢泼德值扩展框架，考虑工艺步骤顺序
- 在实际产线数据中准确识别与缺陷相关的关键检测指标
- 适合半导体制造质量分析与故障溯源场景
当某类晶圆缺陷在质检环节出现时，如何定位上游异常工艺？现代半导体制造包含数千道工序，缺陷的跨工艺根因分析极具挑战。本文提出一种名为轨迹谢泼德归因（Trajectory Shapley Attribution, TSA）的新框架，是可解释人工智能中广泛使用的谢泼德值（Shapley Values, SV）的延伸。TSA克服了传统SV忽略工艺流程顺序性以及依赖任意参考点的关键局限。我们将TSA应用于纽约阿尔巴尼纳米技术中心（NY CREATES Albany NanoTech fab）前端制程中的良品-不良品晶圆诊断任务，旨在识别最相关于异常缺陷发生的测量项（作为工艺参数的代理指标）。
原文摘要 · Abstract (English)
How can we identify problematic upstream processes when a certain type of wafer defect starts appearing at a quality checkpoint? Given the complexity of modern semiconductor manufacturing, which involves thousands of process steps, cross-process root cause analysis for wafer defects has been considered highly challenging. This paper proposes a novel framework called Trajectory Shapley Attribution (TSA), an extension of Shapley values (SV), a widely used attribution algorithm in explainable artificial intelligence research. TSA overcomes key limitations of standard SV, including its disregard for the sequential nature of manufacturing processes and its reliance on an arbitrarily chosen reference point. We applied TSA to a good-bad wafer diagnosis task in experimental front-end-of-line processes at the NY CREATES Albany NanoTech fab, aiming to identify measurement items (serving as proxies for process parameters) most relevant to abnormal defect occurrence.
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