Abstract: Methods and systems for detecting defects on a wafer using defect-specific information are provided. One method includes acquiring information for a target on a wafer. The target includes a pattern of interest formed on the wafer and a known DOI occurring proximate to or in the pattern of interest. The information includes an image of the target on the wafer. The method also includes searching for target candidates on the wafer or another wafer. The target candidates include the pattern of interest. The target and target candidate locations are provided to defect detection. In addition, the method includes detecting the known DOI in the target candidates by identifying potential DOI locations in images of the target candidates and applying one or more detection parameters to images of the potential DOI locations.

Abstract: Methods and systems for detecting defects on a wafer are provided. One method includes determining characteristics of care areas for a wafer based on wafer patterns. Determining the characteristics includes determining locations of care areas, identifying at least one pattern of interest (POI) in the wafer patterns for each of the care areas, allowing any of the care areas to have a free-form shape, allowing the care areas to be larger than frame images and selecting two or more POIs for at least one of the care areas. The method also includes searching for POIs in images generated for the wafer using an inspection system. In addition, the method includes detecting defects on the wafer by determining positions of the care areas in the images and applying one or more defect detection methods to the images based on the positions of the care areas in the images.

Abstract: Systems and methods for classifying defects detected on a wafer are provided. One method includes detecting defects on a wafer based on output generated for the wafer by an inspection system. The method also includes determining one or more attributes for at least one of the defects based on portions of a standard reference image corresponding to the at least one of the defects. The method further includes classifying the at least one of the defects based at least in part on the one or more determined attributes.

Abstract: Methods and systems for detecting defects on a wafer using defect-specific and multi-channel information are provided. One method includes acquiring information for a target on a wafer. The target includes a pattern of interest (POI) formed on the wafer and a known defect of interest (DOI) occurring proximate to or in the POI. The method also includes detecting the known DOI in target candidates by identifying potential DOI locations based on images of the target candidates acquired by a first channel of an inspection system and applying one or more detection parameters to images of the potential DOI locations acquired by a second channel of the inspection system. Therefore, the image(s) used for locating potential DOI locations and the image(s) used for detecting defects can be different.

Abstract: The present invention includes searching imagery data in order to identify one or more patterned regions on a semiconductor wafer, generating one or more virtual Fourier filter (VFF) working areas, acquiring an initial set of imagery data from the VFF working areas, defining VFF training blocks within the identified patterned regions of the VFF working areas utilizing the initial set of imagery data, wherein each VFF training block is defined to encompass a portion of the identified patterned region displaying a selected repeating pattern, calculating an initial spectrum for each VFF training block utilizing the initial set of imagery data from the VFF training blocks, and generating a VFF for each training block by identifying frequencies of the initial spectrum having maxima in the frequency domain, wherein the VFF is configured to null the magnitude of the initial spectrum at the frequencies identified to display spectral maxima.

Abstract: Methods and systems for detecting defects on a wafer using adaptive local thresholding and color filtering are provided. One method includes determining local statistics of pixels in output for a. wafer generated using an inspection system, determining which of the pixels are outliers based on the local statistics, and comparing the outliers to the pixels surrounding the outliers to identify the outliers that do not belong to a cluster of outliers as defect candidates. The method also includes determining a value for a difference in color between the pixels of the defect candidates and the pixels surrounding the defect candidates. The method further includes identifying the defect candidates that have a value for the difference in color greater than or equal to a predetermined value as nuisance defects and the defect candidates that have a value for the difference in color less than the predetermined value as real defects.

Abstract: Methods and systems for detecting defects on a wafer are provided. One method includes determining characteristics of care areas for a wafer based on wafer patterns. Determining the characteristics includes determining locations of care areas, identifying at least one pattern of interest (POI) in the wafer patterns for each of the care areas, allowing any of the care areas to have a free-form shape, allowing the care areas to be larger than frame images and selecting two or more POIs for at least one of the care areas. The method also includes searching for POIs in images generated for the wafer using an inspection system. In addition, the method includes detecting defects on the wafer by determining positions of the care areas in the images and applying one or more defect detection methods to the images based on the positions of the care areas in the images.

Abstract: Methods and systems for detecting defects on a wafer using defect-specific and multi-channel information are provided. One method includes acquiring information for a target on a wafer. The target includes a pattern of interest (POI) formed on the wafer and a known defect of interest (DOI) occurring proximate to or in the POI. The method also includes detecting the known DOI in target candidates by identifying potential DOI locations based on images of the target candidates acquired by a first channel of an inspection system and applying one or more detection parameters to images of the potential DOI locations acquired by a second channel of the inspection system. Therefore, the image(s) used for locating potential DOI locations and the image(s) used for detecting defects can be different.

Abstract: Computer-implemented methods, computer-readable media, and systems for selecting one or more parameters for a defect detection method are provided. One method includes selecting one or more parameters of a defect detection method using an optimization function and information for a set of classified defects, which includes defects of interest and nuisance defects, such that the one or more parameters satisfy an objective for the defect detection method.

Abstract: The present invention includes searching imagery data in order to identify one or more patterned regions on a semiconductor wafer, generating one or more virtual Fourier filter (VFF) working areas, acquiring an initial set of imagery data from the VFF working areas, defining VFF training blocks within the identified patterned regions of the VFF working areas utilizing the initial set of imagery data, wherein each VFF training block is defined to encompass a portion of the identified patterned region displaying a selected repeating pattern, calculating an initial spectrum for each VFF training block utilizing the initial set of imagery data from the VFF training blocks, and generating a VFF for each training block by identifying frequencies of the initial spectrum having maxima in the frequency domain, wherein the VFF is configured to null the magnitude of the initial spectrum at the frequencies identified to display spectral maxima.

Abstract: Various embodiments for determining parameters for wafer inspection and/or metrology are provided.

Abstract: Various computer-implemented methods are provided. One method for sorting defects in a design pattern of a reticle includes searching for defects of interest in inspection data using priority information associated with individual defects in combination with one or more characteristics of a region proximate the individual defects. The priority information corresponds to modulation levels associated with the individual defects. The inspection data is generated by comparing images of the reticle generated for different values of a lithographic variable. The images include at least one reference image and at least one modulated image. A composite reference image can be generated from two or more reference images. The method also includes assigning one or more identifiers to the defects of interest. The identifier(s) may include, for example, a defect classification and/or an indicator identifying if the defects of interest are to be used for further processing.

Abstract: Computer-implemented methods, computer-readable media, and systems for selecting one or more parameters for inspection of a wafer are provided.

Abstract: Various computer-implemented methods are provided. One method for sorting defects in a design pattern of a reticle includes searching for defects of interest in inspection data using priority information associated with individual defects in combination with one or more characteristics of a region proximate the individual defects. The priority information corresponds to modulation levels associated with the individual defects. The inspection data is generated by comparing images of the reticle generated for different values of a lithographic variable. The images include at least one reference image and at least one modulated image. A composite reference image can be generated from two or more reference images. The method also includes assigning one or more identifiers to the defects of interest. The identifier(s) may include, for example, a defect classification and/or an indicator identifying if the defects of interest are to be used for further processing.

Abstract: Various computer-implemented methods are provided. One method for sorting defects in a design pattern of a reticle includes searching for defects of interest in inspection data using priority information associated with individual defects in combination with one or more characteristics of a region proximate the individual defects. The priority information corresponds to modulation levels associated with the individual defects. The inspection data is generated by comparing images of the reticle generated for different values of a lithographic variable. The images include at least one reference image and at least one modulated image. A composite reference image can be generated from two or more reference images. The method also includes assigning one or more identifiers to the defects of interest. The identifier(s) may include, for example, a defect classification and/or an indicator identifying if the defects of interest are to be used for further processing.

Abstract: Methods and systems for binning defects detected on a specimen are provided. One method includes comparing a test image to reference images. The test image includes an image of one or more patterned features formed on the specimen proximate to a defect detected on the specimen. The reference images include images of one or more patterned features associated with different regions of interest within a device being formed on the specimen. If the one or more patterned features of the test image match the one or more patterned features of one of the reference images, the method includes assigning the defect to a bin corresponding to the region of interest associated with the reference image.

Abstract: Methods and systems for binning defects detected on a specimen are provided. One method includes comparing a test image to reference images. The test image includes an image of one or more patterned features formed on the specimen proximate to a defect detected on the specimen. The reference images include images of one or more patterned features associated with different regions of interest within a device being formed on the specimen. If the one or more patterned features of the test image match the one or more patterned features of one of the reference images, the method includes assigning the defect to a bin corresponding to the region of interest associated with the reference image.

Abstract: Disclosed are methods and apparatus for efficiently setting up and maintaining a defect classification system. In general terms, the setup procedure optionally includes automatically grouping a set of provided defects and presenting a representative set from each defect group to the user for classification. After the initial manual classification of the representative defects, the setup procedure includes an automatic procedure for classifying the non-reviewed or unclassified defects based on the manual class codes from the user-reviewed defects. After the automatic classification operation, the user may also be presented with defects from each class which may require re-classification. In particular embodiments, the user is iteratively presented with defects which have classifications that are suspect, which are near classification boundaries, or have classifications that have a low confidence level until each class is pure or contains a same type of defect classes as assigned by the user.

Abstract: Methods and systems for binning defects detected on a specimen are provided. One method includes comparing a test image to reference images. The test image includes an image of one or more patterned features formed on the specimen proximate to a defect detected on the specimen. The reference images include images of one or more patterned features associated with different regions of interest within a device being formed on the specimen. If the one or more patterned features of the test image match the one or more patterned features of one of the reference images, the method includes assigning the defect to a bin corresponding to the region of interest associated with the reference image.

Abstract: Various computer-implemented methods are provided. One method for sorting defects in a design pattern of a reticle includes searching for defects of interest in inspection data using priority information associated with individual defects in combination with one or more characteristics of a region proximate the individual defects. The priority information corresponds to modulation levels associated with the individual defects. The inspection data is generated by comparing images of the reticle generated for different values of a lithographic variable. The images include at least one reference image and at least one modulated image. A composite reference image can be generated from two or more reference images. The method also includes assigning one or more identifiers to the defects of interest. The identifier(s) may include, for example, a defect classification and/or an indicator identifying if the defects of interest are to be used for further processing.