基于晶圆的异常值筛选的一致性

Sebastian Siatkowski, Chuanhe Jay Shan, Li-C. Wang, N. Sumikawa, W. R. Daasch, J. Carulli
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引用次数: 6

摘要

异常值筛选是测试汽车产品的一种流行方法。在实践中,开发离群值模型可能是主观的,使模型的正当性具有挑战性。在本文中,我们提出了一个新的概念,称为一致性,它提供了一种数据驱动的客观方法来评估离群值模型。我们根据这种新的模型一致性概念研究了离群模型的发展,并报告了在汽车生产线上的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Consistency in wafer based outlier screening
Outlier screening is a popular approach for testing automotive products. In practice, developing an outlier model can be subjective, making justification of the model challenging. In this paper we propose a new concept called Consistency which provides a data-driven objective way to assess an outlier model. We study the development of outlier models in view of this new model consistency concept and report experimental findings on an automotive product line.
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