10.6084/m9.figshare.3840657.v2
Ching-Kang Ing
Ching-Kang
Ing
Tze Leung Lai
Tze Leung
Lai
Milan Shen
Milan
Shen
KaWai Tsang
KaWai
Tsang
Shu-Hui Yu
Shu-Hui
Yu
Multiple Testing in Regression Models With Applications to Fault Diagnosis in the Big Data Era
Taylor & Francis Group
2017
Backward elimination
Family-wise error rate
Fault detection and diagnosis
Lasso
Multistage manufacturing process
Orthogonal greedy algorithm
Sparsity
Wafer fabrication
2017-04-17 19:52:27
Dataset
https://tandf.figshare.com/articles/dataset/Multiple_Testing_in_Regression_Models_with_Applications_to_Fault_Diagnosis_in_Big_Data_Era/3840657
<p>Motivated by applications to root-cause identification of faults in multistage manufacturing processes that involve a large number of tools or equipment at each stage, we consider multiple testing in regression models whose outputs represent the quality characteristics of a multistage manufacturing process. Because of the large number of input variables that correspond to the tools or equipments used, this falls in the framework of regression modeling in the modern era of big data. On the other hand, with quick fault detection and diagnosis followed by tool rectification, sparsity can be assumed in the regression model. We introduce a new approach to address the multiple testing problem and demonstrate its advantages over existing methods. We also illustrate its performance in an application to semiconductor wafer fabrication that motivated this development. Supplementary materials for this article are available online.</p>