Wafer Map Pattern Detection Based On Supervised Machine Learning

摘要

Various aspects of the disclosed technology relate to training and applying a machine learning model for defect pattern detection. Defect pattern variants of one or more defect patterns are generated. The one or more defect patterns are extracted from wafer maps of wafers having at least systematic defects. Each of the generated defect pattern variants is superimposed on wafer maps of wafers having no systematic defects to generate positive training data of wafer maps, which are included in a training dataset. Based on the training dataset, a trained machine-learning model for recognizing known defect patterns on wafer maps is derived.