Abstract: There are provided system and method of classifying defects in a specimen. The method includes: obtaining one or more defect clusters detected on a defect map of the specimen, each cluster characterized by a set of cluster attributes comprising spatial attributes including spatial density indicative of density of defects in one or more regions accommodating the cluster, each given defect cluster being detected at least based on the spatial density thereof meeting a criterion. The defect map also comprises non-clustered defects. Defects of interest (DOI) are identified in each cluster by performing respective defect filtrations for each cluster and non-clustered defects.

Abstract: There are provided system and method of classifying defects in a specimen. The method includes: obtaining one or more defect clusters detected on a defect map of the specimen, each cluster characterized by a set of cluster attributes comprising spatial attributes including spatial density indicative of density of defects in one or more regions accommodating the cluster, each given defect cluster being detected at least based on the spatial density thereof meeting a criterion. The defect map also comprises non-clustered defects. Defects of interest (DOI) are identified in each cluster by performing respective defect filtrations for each cluster and non-clustered defects.

Abstract: There are provided system and method of classifying defects in a specimen. The method includes: obtaining one or more defect clusters detected on a defect map of the specimen, each cluster characterized by a set of cluster attributes comprising spatial attributes including spatial density indicative of density of defects in one or more regions accommodating the cluster, each given defect cluster being detected at least based on the spatial density thereof meeting a criterion; for each cluster, applying a cluster classifier to a respective set of cluster attributes thereof to associate the cluster with one or more labels of a predefined set of labels, wherein the cluster classifier is trained using cluster training data; and identifying DOI in each cluster by performing a defect filtration for each cluster using one or more filtering parameters specified in accordance with the label of the cluster.

Abstract: There are provided system and method of classifying defects in a specimen. The method includes: obtaining one or more defect clusters detected on a defect map of the specimen, each cluster characterized by a set of cluster attributes comprising spatial attributes including spatial density indicative of density of defects in one or more regions accommodating the cluster, each given defect cluster being detected at least based on the spatial density thereof meeting a criterion; for each cluster, applying a cluster classifier to a respective set of cluster attributes thereof to associate the cluster with one or more labels of a predefined set of labels, wherein the cluster classifier is trained using cluster training data; and identifying DOI in each cluster by performing a defect filtration for each cluster using one or more filtering parameters specified in accordance with the label of the cluster.

Abstract: A method for image processing includes providing a microscopic image of a structure fabricated on a substrate and computer-aided design (CAD) data used in fabricating the structure. The microscopic image is processed by a computer so as to generate a first directionality map, which includes, for a matrix of points in the microscopic image, respective directionality vectors corresponding to magnitudes and directions of edges at the points irrespective of a sign of the magnitudes. The CAD data are processed by the computer so as to produce a simulated image based on the CAD data and to generate a second directionality map based on the simulated image. The first and second directionality maps are compared by the computer so as to register the microscopic image with the CAD data.

Abstract: A method, system and computer software product for tuning a classification system. The tuning method receives training data including items, each associated with a training class label, and obtains test data including association of each item with an automatic class label and corresponding values of a first confidence level and a second confidence level. Per automatic class, the method generates two or more performance metrics based on the training data and the test data. The method selects, for each automatic class, a preferred pair of values of the first confidence threshold and the second confidence threshold for which, by rejecting all items bellow the first and second thresholds, with respect to all of the automatic classes, a global optimum condition of the performance metrics is met.

Abstract: A method for image processing includes providing a microscopic image of a structure fabricated on a substrate and computer-aided design (CAD) data used in fabricating the structure. The microscopic image is processed by a computer so as to generate a first directionality map, which includes, for a matrix of points in the microscopic image, respective directionality vectors corresponding to magnitudes and directions of edges at the points irrespective of a sign of the magnitudes. The CAD data are processed by the computer so as to produce a simulated image based on the CAD data and to generate a second directionality map based on the simulated image. The first and second directionality maps are compared by the computer so as to register the microscopic image with the CAD data.