Unsupervised density-based machine learning for abnormal leveling signatures detection

Mathias Chastan; Auguste Lam; Franck Iutzeler

doi:10.1117/12.2581468

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

Mathias Chastan, Auguste Lam, Franck Iutzeler

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.

Conference Presentation

Citation Download Citation

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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