Unsupervised density based machine learning for abnormal leveling signatures detection

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.