Machine learning techniques for wafer defect map classification

A system, comprising:

• one or more memories;
• and
• an unsupervised classification circuitry, configured to execute one or more instructions to:
• generate, using a non-linear dimensionality reduction machine learning model and based at least in part on vector representations for a set of wafer defect map images, reduced feature data, wherein the reduced feature data comprises feature data for the set of wafer defect map images in a reduced dimensionality space;
• generate, using a density-based clustering machine learning model and based at least in part on the reduced feature data, one or more wafer defect pattern clusters, wherein each wafer defect map image is associated with a particular wafer defect pattern cluster of the one or more wafer defect pattern clusters;
• and
• generate a classification prediction for each wafer defect map image based at least in part on the particular wafer defect pattern cluster associated with the respective wafer defect map image.