Abstract: Input data may be received. The input data may include an image of a pattern and location data that identifies a modified portion of the pattern. A processing device may determine a first parameter of a first dimension within the pattern and a second parameter of a second dimension outside of the pattern. A combined set may be generated based on the first parameter and the second parameter. A defect associated with the modified portion may be classified based on the combined set.

Abstract: There is provided a system and method for mask inspection, comprising: obtaining a plurality of images, each representative of a respective part of the mask; generating a CD map of the mask comprising a plurality of composite values of a CD measurement of a POI respectively derived from the plurality of images, comprising, for each given image: dividing the given image into a plurality of sections; searching for the POI in the plurality of sections, giving rise to a set of sections, each with presence of at least one of the POI therein; for each section, obtaining a value of the CD measurement using a printing threshold, giving rise to a set of values of the CD measurement corresponding to the set of sections; and combining the set of values to a composite value of the CD measurement corresponding to the given image.

Abstract: There is provided a system and method of a method of mask inspection, comprising: obtaining a first image representative of at least part of the mask; applying a printing threshold on the first image to obtain a second image; estimating a contour for each structural element of interest (SEI) of a group of SEIs, and extracting a set of attributes characterizing the contour, giving rise to a group of contours corresponding to the group of SEIs and respective sets of attributes associated therewith; for each given contour, identifying, among the remaining contours in the group of contours, one or more reference contours similar to the given contour, by comparing between the respective sets of attributes associated therewith; and measuring a deviation between the given contour and each reference contour thereof, giving rise to one or more measured deviations indicative of whether a defect is present.

Abstract: There is provided a mask inspection system and a method of mask inspection. The method comprises: detecting, by the inspection tool, a runtime defect at a defect location on a mask of a semiconductor specimen during runtime scan of the mask, and acquiring, by the inspection tool after runtime and based on the defect location, a plurality sets of aerial images of the runtime defect corresponding to a plurality of focus states throughout a focus process window, each set of aerial images acquired at a respective focus state. The method further comprises for each set of aerial images, calculating a statistic-based EPD value of the runtime defect, thereby giving rise to a plurality of statistic-based EPD values each corresponding to a respective focus state, and determining whether the runtime defect is a true defect based on the plurality of statistic-based EPD values.

Abstract: Input data may be received. The input data may include an image of a pattern and location data that identifies a modified portion of the pattern. A processing device may determine a first parameter of a first dimension within the pattern and a second parameter of a second dimension outside of the pattern. A combined set may be generated based on the first parameter and the second parameter. A defect associated with the modified portion may be classified based on the combined set.

Abstract: There is provided a mask inspection system and a method of mask inspection. The method comprises: during a runtime scan of a mask of a semiconductor specimen, processing a plurality of aerial images of the mask acquired by the mask inspection system to calculate a statistic-based Edge Positioning Displacement (EPD) of a potential defect, wherein the statistic-based EPD is calculated using a Print Threshold (PT) characterizing the mask and is applied to each of the one or more acquired aerial images to calculate respective EPD of the potential defect therein; and filtering the potential defect as a “runtime true” defect when the calculated statistic-based EPD exceeds a predefined EPD threshold, and filtering out the potential defect as a “false” defect when the calculated statistic-based EPD is lower than the predefined EPD threshold. The method can further comprise after-runtime EPD-based filtering of the plurality of “runtime true” defects.

Abstract: A captured image of a pattern and a reference image of the pattern may be received. A contour of interest of the pattern may be identified. One or more measurements of a dimension of the pattern may be determined for each of the reference image and the captured image with respect to the contour of interest of the pattern. A defect associated with the contour of interest may be classified based on the determined one or more measurements of the dimension of the pattern for each of the reference image and the captured image.

Abstract: There is provided a mask inspection system and a method of mask inspection. The method comprises: during a runtime scan of a mask of a semiconductor specimen, processing a plurality of aerial images of the mask acquired by the mask inspection system to calculate a statistic-based Edge Positioning Displacement (EPD) of a potential defect, wherein the statistic-based EPD is calculated using a Print Threshold (PT) characterizing the mask and is applied to each of the one or more acquired aerial images to calculate respective EPD of the potential defect therein; and filtering the potential defect as a “runtime true” defect when the calculated statistic-based EPD exceeds a predefined EPD threshold, and filtering out the potential defect as a “false” defect when the calculated statistic-based EPD is lower than the predefined EPD threshold. The method can further comprise after-runtime EPD-based filtering of the plurality of “runtime true” defects.

Abstract: A captured image of a pattern and a reference image of the pattern may be received. A contour of interest of the pattern may be identified. One or more measurements of a dimension of the pattern may be determined for each of the reference image and the captured image with respect to the contour of interest of the pattern. A defect associated with the contour of interest may be classified based on the determined one or more measurements of the dimension of the pattern for each of the reference image and the captured image.

Abstract: There are provided system and method of generating an examination recipe usable for examining a specimen, the method comprising: capturing images from dies and obtaining noise map indicative of noise distribution on the images; receiving design data representative of a plurality of design groups each having the same design pattern; calculating a group score for each given design group, the group score calculated based on the noise data associated with the given design group and a defect budget allocated for area of the given design group; providing segmentation related to the dies, comprising: associating design groups with segmentation labels indicative of different noise levels based on the group score, thereby obtaining a set of die segments each corresponding to one or more design groups associated with the same segmentation label and segmentation configuration data; and generating an examination recipe using the segmentation configuration data.

Abstract: There are provided system and method of generating an examination recipe usable for examining a specimen, the method comprising: capturing images from dies and obtaining noise map indicative of noise distribution on the images; receiving design data representative of a plurality of design groups each having the same design pattern; calculating a group score for each given design group, the group score calculated based on the noise data associated with the given design group and a defect budget allocated for area of the given design group; providing segmentation related to the dies, comprising: associating design groups with segmentation labels indicative of different noise levels based on the group score, thereby obtaining a set of die segments each corresponding to one or more design groups associated with the same segmentation label and segmentation configuration data; and generating an examination recipe using the segmentation configuration data.

Abstract: Methods, systems, and computer program products for signature detection. One example of a method includes: acquiring an article defect density map comprising a plurality of sections corresponding to a first resolution level which is indicative of defect numbers for the sections, and determining a distribution representative of the defect numbers or function thereof; determining a threshold in accordance with said distribution, and identifying sections, out of said plurality of sections in the article defect density map, with defect numbers or function thereof above the threshold; and clustering at least part of adjoining identified sections, into one or more signatures, thus detecting said one or more signatures.

Abstract: Methods, systems, and computer program products for signature detection. One example of a method includes: acquiring an article defect density map comprising a plurality of sections corresponding to a first resolution level which is indicative of defect numbers for the sections, and determining a distribution representative of the defect numbers or function thereof; determining a threshold in accordance with said distribution, and identifying sections, out of said plurality of sections in the article defect density map, with defect numbers or function thereof above the threshold; and clustering at least part of adjoining identified sections, into one or more signatures, thus detecting said one or more signatures.