Abstract: A defect detection system for computerized detection of defects in an inspected object based on processing of an inspection image generated by collecting signals arriving from the inspected object, the system including: an interface for obtaining an inspected noise-indicative value and multiple reference noise-indicative values, the inspected noise-indicative value representative of an analyzed pixel and each of the reference noise-indicative values representative of a reference pixel among a plurality of reference pixels; and a processor, including: a noise analysis module, configured to compute a representative noise-indicative value based on a plurality of noise-indicative values which includes the inspected noise-indicative value and the multiple reference noise-indicative values; and a defect analysis module, configured to calculate a defect-indicative value based on an inspected value representative of the analyzed pixel, and to determine a presence of a defect in the analyzed pixel based on the represe

Abstract: A defect detection system for computerized detection of defects, the system including: an interface for receiving inspection image data including information of an analyzed pixel and of a plurality of reference pixels; and a processor, including: a differences analysis module, configured to: (a) calculate differences based on an inspected value representative of the analyzed pixel and on multiple reference values, each of which is representative of a reference pixel among the plurality of reference pixels; wherein the differences analysis module is configured to calculate for each of the reference pixels a difference between the reference value of the reference pixel and the inspected value; and (b) compute a representative difference value based on a plurality of the differences; and a defect analysis module, configured to determine a presence of a defect in the analyzed pixel based on the representative difference value.

Abstract: A system capable of inspecting an article for defects, the system including: a patch comparator, configured to determine with respect to each of a plurality of reference patches in a reference image a similarity level, based on a predefined patch-similarity criterion and on a source patch defined in the reference image; an evaluation module, configured to rate each inspected pixel out of multiple inspected pixels of the inspection image with a representative score which is based on the similarity level of a reference patch associated with a reference pixel corresponding to the inspected pixel; a selection module, configured to select multiple selected inspected pixels based on the representative scores of the multiple inspected pixels; and a defect detection module, configured to determine a presence of a defect in the candidate pixel based on an inspected value of the candidate pixel and inspected values of the selected inspected pixels.

Abstract: There is provided an inspection method capable of classifying defects detected on a production layer of a specimen. The method comprises: obtaining input data related to the detected defects; processing the input data using a decision algorithm associated with the production layer and specifying two or more classification operations and a sequence thereof; and sorting the processed defects in accordance with predefined bins, wherein each bin is associated with at least one classification operation, wherein at least one classification operation sorts at least part of the processed defects to one or more classification bins to yield finally classified defects, and wherein each classification operation, excluding the last one, sorts at least part of the processed defects to be processed by one or more of the following classification operations.

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 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 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.

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 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 inspecting a plurality of dies, that are typically disposed on a surface of a semiconducting wafer. Each of the dies includes respective functional features within the die. The method consists of identifying within a first die a first multiplicity of the functional features having respective characteristics, and measuring respective first locations of the first multiplicity with respect to an origin of the first die. Within a group of second dies a second multiplicity of the functional features having the respective characteristics is identified, respective second locations of the second multiplicity are measured. The second locations are compared to the first locations to determine a location of an origin of the group of the second dies.

Abstract: A method for inspecting a plurality of dies, that are typically disposed on a surface of a semiconducting wafer. Each of the dies includes respective functional features within the die. The method consists of identifying within a first die a first multiplicity of the functional features having respective characteristics, and measuring respective first locations of the first multiplicity with respect to an origin of the first die. Within a group of second dies a second multiplicity of the functional features having the respective characteristics is identified, respective second locations of the second multiplicity are measured. The second locations are compared to the first locations to determine a location of an origin of the group of the second dies.

Abstract: A method to extend the process monitoring capabilities of a semiconductor wafer optical inspection system so as to be able to detect low-resolution effects of process variations over the surface of a wafer at much higher sensitivity than heretofore possible. The method consists, in essence, of grouping sensed pixels by geometric blocks over the inspected surface and comparing each block with a corresponding one from another die on the same wager, from another wager of from a stored model image. In one embodiment of the invention, pixel values are compared directly and differences are thresholded at a considerably lower level than during a defects detection process. In another embodiment, there is calculated a signature for each block, based on the sensed light intensity values, and corresponding signatures are compared.

Abstract: A method to extend the process monitoring capabilities of a semiconductor wafer optical inspection system so as to be able to detect low-resolution effects of process variations over the surface of a wafer at much higher sensitivity than heretofore possible. The method consists, in essence, of grouping sensed pixels by geometric blocks over the inspected surface and comparing each block with a corresponding one from another die on the same wager, from another wager of from a stored model image. In one embodiment of the invention, pixel values are compared directly and differences are thresholded at a considerably lower level than during a defects detection process. In another embodiment, there is calculated a signature for each block, based on the sensed light intensity values, and corresponding signatures are compared.

Abstract: A method to extend the process monitoring capabilities of a semiconductor wafer optical inspection system so as to be able to detect low-resolution effects of process variations over the surface of a wafer at much higher sensitivity than heretofore possible. The method consists, in essence, of grouping sensed pixels by geometric blocks over the inspected surface and comparing each block with a corresponding one from another die on the same wager, from another wager of from a stored model image. In one embodiment of the invention, pixel values are compared directly and differences are thresholded at a considerably lower level than during a defects detection process. In another embodiment, there is calculated a signature for each block, based on the sensed light intensity values, and corresponding signatures are compared.

Abstract: A method to extend the process monitoring capabilities of a semiconductor wafer optical inspection system so as to be able to detect low-resolution effects of process variations over the surface of a wafer at much higher sensitivity than heretofore possible. The method consists, in essence, of grouping sensed pixels by geometric blocks over the inspected surface and comparing each block with a corresponding one from another die on the same wager, from another wager of from a stored model image. In one embodiment of the invention, pixel values are compared directly and differences are thresholded at a considerably lower level than during a defects detection process. In another embodiment, there is calculated a signature for each block, based on the sensed light intensity values, and corresponding signatures are compared.

Abstract: A method to extend the process monitoring capabilities of a semiconductor wafer optical inspection system so as to be able to detect low-resolution effects of process variations over the surface of a wafer at much higher sensitivity than heretofore possible. The method consists, in essence, of grouping sensed pixels by geometric blocks over the inspected surface and comparing each block with a corresponding one from another die on the same wafer, from another wafer or from a stored model image. In one embodiment of the invention, pixel values are compared directly and differences are thresholded at a considerably lower level than during a defects detection process. In another embodiment, there is calculated a signature for each block, based on the sensed light intensity values, and corresponding signatures are compared.

Abstract: A method to extend the process monitoring capabilities of a semiconductor wafer optical inspection system so as to be able to detect low-resolution effects of process variations over the surface of a wafer at much higher sensitivity than heretofore possible. The method consists, in essence, of grouping sensed pixels by geometric blocks over the inspected surface and comparing each block with a corresponding one from another die on the same wafer, from another wafer or from a stored model image. In one embodiment of the invention, pixel values are compared directly and differences are thresholded at a considerably lower level than during a defects detection process. In another embodiment, there is calculated a signature for each block, based on the sensed light intensity values, and corresponding signatures are compared.