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Proceedings Paper

Outlier detection in contamination control
Author(s): Jeffrey Weintraub; Scott Warrick
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Paper 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.

Paper Details

Date Published: 13 March 2018
PDF: 15 pages
Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105851T (13 March 2018); doi: 10.1117/12.2297379
Show Author Affiliations
Jeffrey Weintraub, Cirrus Logic, Inc. (United States)
Scott Warrick, Cirrus Logic, Inc. (United States)


Published in SPIE Proceedings Vol. 10585:
Metrology, Inspection, and Process Control for Microlithography XXXII
Vladimir A. Ukraintsev, Editor(s)

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