Abstract: A method and apparatus for training a learning model for automatic defect detection and classification of at least a portion of a processed wafer include receiving labeled images having defect classification types and features for portions of a post-processed wafer, creating a first training set comprising the received labeled images, training the machine learning model to automatically classify wafer portions based on at least one detected defect in respective wafer portions using the first training set, receiving labeled wafer profiles having respective downstream yield data, creating a second training set comprising the labeled wafer profiles and training the machine learning model, using the second training set, to automatically determine a respective downstream yield of a wafer based on a respective wafer profile. The machine learning model can be applied to at least one unlabeled wafer image to determine at least one defect classification for at least one portion of a wafer.

Abstract: A method, apparatus and system for the automatic detection and measurement of chipping defects on diced wafers includes receiving an image of at least a portion of a diced wafer, aligning the received image of the at least the portion of the diced wafer, determining edges of the at least the portion of the diced wafer depicted in the aligned, received image, automatically determining at least one baseline from which to measure chipping defects on the at least the portion of the diced wafer from the determined edges, and measuring chipping defects on the at least the portion of the diced wafer using at least one determined, respective baseline. In some embodiments, the method, apparatus and system can further include applying a machine learning model to measured chipping defects to determine if a critical failure exists on the diced wafer.

Abstract: A method and apparatus for training a learning model for the automatic detection and classification of defects on wafers includes receiving labeled images of wafer defects having multiple defect classifications, creating a first training set including the received labeled images of wafer defects, training the machine learning model to automatically detect and classify wafer defects in a first stage using the first training set, blending at least one set of at least two labeled images having different classifications to generate additional labeled image data, creating a second training set including the blended, additional labeled image data, and training the machine learning model to automatically detect and classify wafer defects in a second stage using the second training set. The trained machine learning model can then be applied to at least one unlabeled wafer image to determine at least one defect classification for the at least one unlabeled wafer image.