用多元检验分析筛选客户回报

N. Sumikawa, J. Tikkanen, Li-C. Wang, L. Winemberg, M. Abadir
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引用次数: 42

摘要

这项工作研究了基于参数晶圆排序测试测量的多变量分析构建的模型捕获客户回报的潜力。在这样的分析中,选择测试子集来构建模型,以做出通过/失败的决策。考虑了两种方法。先发制人的方法是选择相关检验来构建多变量检验模型,从而筛选出异常值。这种方法不依赖于已知的客户回报。相反,反应性方法选择与给定客户回报相关的测试,并构建特定于该回报的离群值模型。该模型用于捕获与收益相似的未来部分。该研究基于一款面向汽车市场的高质量SoC生产过程中收集的大约16个月的测试数据。数据由62个客户退货组成,属于52批次。研究表明,每种方法都能获得另一种方法无法获得的回报。通过这两种方法,研究表明,多变量检验分析对降低客户回头率有显著的影响,特别是在生产后期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Screening customer returns with multivariate test analysis
This work studies the potential of capturing customer returns with models constructed based on multivariate analysis of parametric wafer sort test measurements. In such an analysis, subsets of tests are selected to build models for making pass/fail decisions. Two approaches are considered. A preemptive approach selects correlated tests to construct multivariate test models to screen out outliers. This approach does not rely on known customer returns. In contrast, a reactive approach selects tests relevant to a given customer return and builds an outlier model specific to the return. This model is applied to capture future parts similar to the return. The study is based on test data collected over roughly 16 months of production for a high-quality SoC sold to the automotive market. The data consists of 62 customer returns belonging to 52 lots. The study shows that each approach can capture returns not captured by the other. With both approaches, the study shows that multivariate test analysis can have a significant impact on reducing customer return rates especially during the later period of the production.
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