Abstract: Methods and systems for organic defect source identification using artificial intelligence (AI) are provided. One or more images depicting a surface of a substrate processed using one or more manufacturing equipment is provided as input to an artificial intelligence (AI) model. Output(s) of the AI model is obtained, where the output(s) indicate defect characterization data associated with a defect detected on the surface of the substrate depicted by the one or more images. A determination is made of whether a level of confidence of at least one defect type and associated defect source from the defect characterization data satisfies one or more confidence criteria. Upon a determination that at least one defect type and associated defect source satisfy the one or more confidence criteria, the at least one defect type and associated defect source are provided to a client device for presentation to one or more users.

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 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 includes receiving, by a processing device, data indicative of one or more defects of a substrate processing in a substrate processing system using a process recipe, the data having a data type. The method further includes processing the data using a trained machine learning model that outputs information about the one or more defects. The method further includes determining one or more possible root causes for the one or more defects based at least in part on the information. The method further includes outputting a sequence of maintenance operations to be performed on the substrate processing system based on the one or more possible root causes for the one or more defects.

Abstract: A method includes receiving first information about one or more defects of a substrate processed using a process recipe. The method further includes processing the first information using a trained machine learning model that outputs matches to historical defects and score values for the matches. The method further includes receiving user input selecting a subset of the one or more matches. The method further includes updating the trained machine learning model based on the user input. The method further includes reprocessing the first information using the updated trained machine learning model that outputs one or more updated matches to historical defects and updated score values for the matches. The method further includes outputting an indication of one or more maintenance operations associated with the one or more updated matches to resolve one or more root causes associated with at least one of the one or more updated matches.

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.

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 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.