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.
Jeffrey Weintraub and Scott Warrick, "Outlier detection in contamination control," Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105851T (Presented at SPIE Advanced Lithography: March 01, 2018; Published: 13 March 2018); https://doi.org/10.1117/12.2297379.
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