13 March 2018 Outlier detection in contamination control
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Abstract
A machine-learning model is presented that effectively partitions historical process data into outlier and inlier subpopulations. This is necessary in order to avoid using outlier data to build a model for detecting process instability. Exact control limits are given without recourse to approximations and the error characteristics of the control model are derived. A worked example for contamination control is presented along with the machine learning algorithm used and all the programming statements needed for implementation.
Conference Presentation
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Jeffrey Weintraub, Scott Warrick, "Outlier detection in contamination control", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105851T (13 March 2018); doi: 10.1117/12.2297379; https://doi.org/10.1117/12.2297379
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