Abstract: A computing system generates a training data set for training a machine learning model to detect line defects on a surface of a specimen. The computing system trains the machine learning model to detect line defects based on the training data set. The machine learning model is trained to detect line defects by detecting the individual defects represented by the plurality of annotations extending between the starting point and the end point. The computing system determines that the machine learning model has exceeded a threshold level of accuracy. Based on the determining, the computing system deploys the machine learning model to detect line defects on future specimens.

Abstract: A computer-implemented method for detecting defects on specimens in an inspection system is disclosed herein. A first set of images of a plurality of specimens having defects formed thereon is received. A second set of images of the plurality of specimens is received, the second set of images includes the plurality of specimens after undergoing a destructive etch process and labels corresponding to each defect. Labels from the second set of images are transferred to the first set of images. A machine learning model is trained to classify defects on unetched specimens based on the first set of images and the labeled first set of images. Once the machine learning model has achieved a threshold of accuracy, the machine learning model may be deployed in the inspection system.

Abstract: A computing system generates a training data set for training a machine learning model to detect line defects on a surface of a specimen. The computing system trains the machine learning model to detect line defects based on the training data set. The machine learning model is trained to detect line defects by detecting the individual defects represented by the plurality of annotations extending between the starting point and the end point. The computing system determines that the machine learning model has exceeded a threshold level of accuracy. Based on the determining, the computing system deploys the machine learning model to detect line defects on future specimens.

Abstract: A computing system receives, from an inspection device, an image of a multi-device substrate having one or more regions. Each region of the one or more regions having a plurality of devices formed thereon. The computing system identifies a golden die corresponding to the multi-device substrate. The golden die represents a canonical design of devices formed on the multi-device substrate. The computing system analyzes the image to identify defects in the image of the multi-device substrate by comparing the image of the multi-device substrate to the golden die. The computing system maps locations of the identified defects to corresponding locations on the golden die. The computing system, based on the mapping, determines, by the computing system, one or more devices of the plurality of devices affected by the defects. The computing system generates, as output, an indication of the defects and the one or more devices affected by the defects.