Unsupervised density-based machine learning for abnormal leveling signatures detection

Mathias Chastan; Auguste Lam; Franck Iutzeler

doi:10.1117/12.2581468

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22 February 2021 Unsupervised density-based machine learning for abnormal leveling signatures detection

Mathias Chastan, Auguste Lam, Franck Iutzeler

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Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116111N (2021) https://doi.org/10.1117/12.2581468
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

The Semiconductor industry relies on the metrology to keep up with a highly competitive production environment and technology ramp up. To reduce metrology costs without degrading quality we propose to use sensors data such as scanner leveling data as a new way to detect maverick lots and wafers enabling a smarter measurement sampling scheme. To achieve this, data preparation and data cleaning with Zernike polynomials method is required. Then the pre-processed data are used to feed an unsupervised density based machine learning algorithm (DBSCAN) that can detect outliers as an human expert would. Finally, a solution (Random Forest Discriminant Analysis) for root cause detection of abnormal fingerprints is tested in this paper. A method working on other use cases (Partial Least Square Discriminant Analysis) is also used for result crossing.

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Mathias Chastan, Auguste Lam, and Franck Iutzeler "Unsupervised density-based machine learning for abnormal leveling signatures detection", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116111N (22 February 2021); https://doi.org/10.1117/12.2581468

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