Abstract: There is provided a system and method of defect detection of a semiconductor specimen. The method includes obtaining a first image of the specimen acquired at a first bit depth, converting by a first processor the first image to a second image with a second bit depth lower than the first bit depth, transmitting the second image to a second processor configured to perform first defect detection on the second image using a first defect detection algorithm to obtain a first set of defect candidates, and sending locations of the first set of defect candidates to the first processor, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations, and performing second defect detection on the set of image patches using a second defect detection algorithm to obtain a second set of defect candidates.

Abstract: There is provided a system and method of examination of a semiconductor specimen. The method includes obtaining an e-beam image representative of a given layer of a given structure on the specimen in runtime, processing at least the e-beam image using a ML model, and obtaining yield related prediction with respect to the given structure prior to performing an electrical test. The ML model is previously trained using a training set comprising multiple stacks of e-beam images corresponding to multiple sites of the given structure on one or more training specimens, each stack of e-beam images representative of the at least given layer of a respective site; and test data acquired from an electrical test performed at the multiple sites and related to actual yield of the training specimens, the test data respectively correlated with the stacks of e-beam images and used as ground truth thereof.

Abstract: There is provided a system and method of examination of semiconductor specimens. The method includes generating a sequence of anomaly scores corresponding to a sequence of specimens sequentially fabricated and examined during a fabrication process, comprising, for each given specimen: obtaining an image of the given specimen acquired by an examination tool; using a machine learning (ML) model to process the image and obtaining an anomaly map indicative of pattern variation in the image; and deriving, based on the anomaly map, an anomaly score indicative of level of pattern variation presented in the given specimen, wherein the anomaly score is correlated with a defectivity score related to defect detection in a correlation relationship, and has higher detection sensitivity than the defectivity score; and analyzing the sequence of anomaly scores to monitor on-going process stability, thereby providing defect related prediction along the fabrication process based on the correlation relationship.

Abstract: There is provided a system and method of defect detection of a semiconductor specimen. The method includes obtaining a first image of the specimen acquired at a first bit depth, converting by a first processor the first image to a second image with a second bit depth lower than the first bit depth, transmitting the second image to a second processor configured to perform first defect detection on the second image using a first defect detection algorithm to obtain a first set of defect candidates, and sending locations of the first set of defect candidates to the first processor, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations, and performing second defect detection on the set of image patches using a second defect detection algorithm to obtain a second set of defect candidates.

Abstract: A method for selecting network documents as a medium for promotional content. The method comprises capturing a plurality of browsing sessions of a plurality of network users in a communication network, each the browsing session mapping consecutive access to a group of the plurality of network documents by one of the plurality of network users, clustering the plurality of network documents in a plurality of clusters according to the plurality of browsing sessions, selecting at least one of the plurality of clusters as a medium for promotional content, and outputting the at least one selected cluster.

Abstract: Non-deterministic text with average word recognition precision below 50% is processed utilizing non-textual differences between words or sequences of words in the text to provide more useful information to users by resolving more than two decision options. One or more indexes that indicate non-textual differences between n-word sequences, where n is a positive integer, may be generated for use in data mining that considers the non-textual differences. Alternatively, multiple indexes may be generated using different data mining techniques that may or may not utilize non-textual differences and then the results produced by the different data mining techniques may be merged to identify non-textual differences. These techniques may be used in classifying, labeling, categorizing, filtering, clustering, or retrieving documents, or in discovering salient terms in a set of documents.