Abstract: The present disclosure provides methods, systems, and media for accurate and efficient estimation of a genome of a genus.

Abstract: The present disclosure provides methods, systems, and media for accurate and efficient estimation of a genome of a genus.

Abstract: The present disclosure provides methods, systems, and media for accurate and efficient estimation of a genome of a genus.

Abstract: Data indicative of alignment targets may be received. Each alignment target may be associated with a target location on an object. Locations of the object to be inspected may be identified. An alignment target from the alignment targets may be selected. Each of the locations may be within a determined distance from the selected alignment target. An indication may be provided to align the object relative to an examination tool for inspecting the locations within the determined distance from the selected alignment target.

Abstract: Data indicative of alignment targets may be received. Each alignment target may be associated with a target location on an object. Locations of the object to be inspected may be identified. An alignment target from the alignment targets may be selected. Each of the locations may be within a determined distance from the selected alignment target. An indication may be provided to align the object relative to an examination tool for inspecting the locations within the determined distance from the selected alignment target.

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: A method, computerized system and computer program product for examining an object using a processor operatively connected to a memory, the method comprising: accommodating in the memory data indicative of a plurality of alignment targets, each alignment target associated with a target location on an object; accommodating in the memory a plurality of locations to be captured; and selecting by the processor an alignment target subset of the plurality of alignment targets, such that each of the plurality of locations is associated with and is within a determined distance from a single alignment target from the alignment target subset, the distance determined in accordance with a provided field of view, and wherein the alignment target subset comprises fewer targets than locations to be reviewed, the alignment target being usable for aligning the object relative to an examination tool for capturing the locations associated with the single alignment target.

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: 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 computer creates a recipe for a manufacturing tool based on design data. The computer obtains the design data, which includes basic elements and hierarchical levels corresponding to the basic elements. The computer selects one or more basic elements of interest and generates one or more sets of simple array cells corresponding to a level of interest. The computer uses the sets of simple array cells to identify periodical areas in level-of-interest coordinates to enable automated recipe creation. The periodical areas are identified with respect to one or more basic elements.

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: There is provided a computer-implemented method of creating a recipe for a manufacturing tool and a system thereof. The method comprises: upon obtaining data characterizing periodical sub-arrays in one or more dies, generating candidate stitches; identifying one or more candidate stitches characterized by periodicity characteristics satisfying, at least, a periodicity criterion, thereby identifying periodical stitches among the candidate stitches; and aggregating the identified periodical stitches and the periodical sub-arrays into periodical arrays, said periodical arrays to be used for automated recipe creation.

Abstract: There is provided a computer-implemented method of creating a recipe for a manufacturing tool and a system thereof. The method comprises: upon obtaining data characterizing periodical sub-arrays in one or more dies, generating candidate stitches; identifying one or more candidate stitches characterized by periodicity characteristics satisfying, at least, a periodicity criterion, thereby identifying periodical stitches among the candidate stitches; and aggregating the identified periodical stitches and the periodical sub-arrays into periodical arrays, said periodical arrays to be used for automated recipe creation.

Abstract: A computer creates a recipe for a manufacturing tool based on design data. The computer obtains the design data, which includes basic elements and hierarchical levels corresponding to the basic elements. The computer selects one or more basic elements of interest and generates one or more sets of simple array cells corresponding to a level of interest. The computer uses the sets of simple array cells to identify periodical areas in level-of-interest coordinates to enable automated recipe creation. The periodical areas are identified with respect to one or more basic elements.

Abstract: A method for inspecting a patterned surface employs reference data related to the pattern to provide a map for identifying regions which are expected to generate equivalent images. These regions are then compared in an image-to-image comparison to identify possible defects. In a first implementation, the regions are related by local symmetry operators. In a second, disjoint corner features or other features are classified and similar features compared.

Abstract: A method for inspecting a patterned surface employs reference data related to the pattern to provide a map for identifying regions which are expected to generate equivalent images. These regions are then compared in an image-to-image comparison to identify possible defects. In a first implementation, the regions are related by local symmetry operators. In a second, disjoint corner features or other features are classified and similar features compared.