Abstract: A method for process analysis includes acquiring first inspection data, using a first inspection modality, with respect to a substrate having multiple instances of a predefined pattern of features formed thereon using different, respective sets of process parameters. Characteristics of defects identified in the first inspection data are processed so as to select a first set of defect locations in which the first inspection data are indicative of an influence of the process parameters on the defects. Second inspection data are acquired, using a second inspection modality having a finer resolution than the first inspection modality, of the substrate at the locations in the first set. The defects appearing in the second inspection data are analyzed so as to select, from within the first set of the locations, a second set of the locations in which the second inspection data are indicative of an optimal range of the process parameters.

Abstract: A method for classifying defects of a wafer, the method is executed by a computerized system, the method may include obtaining defect candidate information about a group of defect candidates, wherein the defect candidate information comprises values of attributes per each defect candidate of the group; selecting, by a processor of the computerized system, a selected sub-group of defect candidates in response to values of attributes of defect candidates that belong to at least the selected sub-group; classifying defect candidates of the selected sub-group to provide selected sub-group classification results; repeating, until fulfilling a stop condition: selecting an additional selected sub-group of defect candidates in response to (a) values of attributes of defect candidates that belong to at least the additional selected sub-group; and (b) classification results obtained from classifying at least one other selected sub-group; and classifying defect candidates of the additional selected sub-group to provide a

Abstract: A method for classifying defects of a wafer, the method is executed by a computerized system, the method may include obtaining defect candidate information about a group of defect candidates, wherein the defect candidate information comprises values of attributes per each defect candidate of the group; selecting, by a processor of the computerized system, a selected sub-group of defect candidates in response to values of attributes of defect candidates that belong to at least the selected sub-group; classifying defect candidates of the selected sub-group to provide selected sub-group classification results; repeating, until fulfilling a stop condition: selecting an additional selected sub-group of defect candidates in response to (a) values of attributes of defect candidates that belong to at least the additional selected sub-group; and (b) classification results obtained from classifying at least one other selected sub-group; and classifying defect candidates of the additional selected sub-group to provide a

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: Inspection apparatus includes an imaging module, which is configured to capture images of defects at different, respective locations on a sample. A processor is coupled to process the images so as to automatically assign respective classifications to the defects, and to autonomously control the imaging module to continue capturing the images responsively to the assigned classifications.

Abstract: A method for classification includes receiving inspection data associated with a plurality of defects found in one or more samples and receiving one or more benchmark classification comprising a class for each of the plurality of defects. a readiness criterion for one or more of the classes is evaluated based on the one or more benchmark classification results, wherein the readiness criterion comprises for each class, a suitability of the inspection data for training an automatic defect classifier for the class. A portion of the inspection data is selected corresponding to one or more defects associated with one or more classes that satisfy the readiness criterion. One or more automatic classifiers are trained for the one or more classes that satisfy the readiness criterion using the selected portion of the inspection data.

Abstract: A method for defect classification includes storing, in a computer system, a definition of a region in a feature space. The definition is associated with a class of defects and comprises a kernel function comprising a parameter. The parameter determines a shape of the region. A confidence threshold for automatic classification of at least one defect associated with the class is received. A value of the parameter associated with the confidence threshold is selected. Inspection data for a plurality of defects detected in one or more samples under inspection is received. The plurality of defects for the class are automatically classified using the kernel function and the selected value of the parameter.

Abstract: A method for classification includes receiving an image of an area of a semiconductor wafer on which a pattern has been formed, the area containing an image location of interest, and receiving computer-aided design (CAD) data relating to the pattern comprising a CAD location of interest corresponding to the image location of interest. At least one value for one or more attributes of the image location of interest is computed based on a context of the CAD location of interest with respect to the CAD data.

Abstract: A method for classification includes receiving an image of an area of a semiconductor wafer on which a pattern has been formed, the area containing a location of interest. At least one value for one or more attributes of the location of interest are computed based upon topographical features of the location of interest in a three-dimensional (3D) map of the area.

Abstract: A method for defect classification includes storing definitions of defect classes in terms of a classification rules in a multi-dimensional feature space. Inspection data associated with defects detected in one or more samples under inspection is received. A plurality of first classification results is generated by applying an automatic classifier to the inspection data based on the definitions, the plurality of first classification results comprising a class label and a corresponding confidence level for a defect. Upon determining that a confidence level for a defect is below a predetermined confidence threshold, a plurality of second classification results are generated by applying at least one inspection modality to the defect. A report is generated comprising a distribution of the defects among the defect classes by combining the plurality of first classification results and the plurality of second classification results.