Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.

Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.

Abstract: Methods and systems for shape metric based scoring of wafer locations are provided. One method includes selecting shape based grouping (SBG) rules for at least two locations on a wafer. For one of the wafer locations, the selecting step includes modifying distances between geometric primitives in a design for the wafer with metrology data for the one location and determining metrical complexity (MC) scores for SBG rules associated with the geometric primitives in a field of view centered on the one location based on the distances. The selecting step also includes selecting one of the SBG rules for the one location based on the MC scores. The method also includes sorting the at least two locations on the wafer based on the SBG rule selected for the at least two locations.

Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.

Abstract: Techniques are provided that can select defects based on criticality of design pattern as well as defect attributes for process window qualification (PWQ). Defects are sorted into categories based on process conditions and similarity of design. Shape based grouping can be performed on the random defects. Highest design based grouping scores can be assigned to the bins, which are then sorted. Particular defects can be selected from the bins. These defects may be reviewed.

Abstract: Methods and systems for shape metric based scoring of wafer locations are provided. One method includes selecting shape based grouping (SBG) rules for at least two locations on a wafer. For one of the wafer locations, the selecting step includes modifying distances between geometric primitives in a design for the wafer with metrology data for the one location and determining metrical complexity (MC) scores for SBG rules associated with the geometric primitives in a field of view centered on the one location based on the distances. The selecting step also includes selecting one of the SBG rules for the one location based on the MC scores. The method also includes sorting the at least two locations on the wafer based on the SBG rule selected for the at least two locations.

Abstract: Methods and systems for determining one or more characteristics for defects detected on a specimen are provided. One system includes one or more computer subsystems configured for identifying a first defect that was detected on a specimen by an inspection system with a first mode but was not detected with one or more other modes. The computer subsystem(s) are also configured for acquiring, from the storage medium, one or more images generated with the one or more other modes at a location on the specimen corresponding to the first defect. In addition, the computer subsystem(s) are configured for determining one or more characteristics of the acquired one or more images and determining one or more characteristics of the first defect based on the one or more characteristics of the acquired one or more images.

Abstract: Techniques are provided that can select defects based on criticality of design pattern as well as defect attributes for process window qualification (PWQ). Defects are sorted into categories based on process conditions and similarity of design. Shape based grouping can be performed on the random defects. Highest design based grouping scores can be assigned to the bins, which are then sorted. Particular defects can be selected from the bins. These defects may be reviewed.

Abstract: Methods and systems for detecting anomalies in images of a specimen are provided. One system includes one or more computer subsystems configured for acquiring images generated of a specimen by an imaging subsystem. The computer subsystem(s) are also configured for determining one or more characteristics of the acquired images. In addition, the computer subsystem(s) are configured for identifying anomalies in the images based on the one or more determined characteristics without applying a defect detection algorithm to the images or the one or more characteristics of the images.

Abstract: Criticality of a detected defect can be determined based on context codes. The context codes can be generated for a region, each of which may be part of a die. Noise levels can be used to group context codes. The context codes can be used to automatically classify a range of design contexts present on a die without needing certain information a priori.

Abstract: Noise induced by pattern-of-interest (POI) image registration and POI vicinity design patterns in intra-die inspection is reduced. POI are grouped into alignment groups by co-occurrence of proximate registration targets. The alignment groups are registered using the co-occurrence of proximate registration targets. Registration by voting is performed, which can measure a degree that each of the patterns-of-interest is an outlier. POI are grouped into at least one vicinity group with same vicinity design effects.

Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.

Abstract: Shape primitives are used for inspection of a semiconductor wafer or other workpiece. The shape primitives can define local topological and geometric properties of a design. One or more rules are applied to the shape primitives. The rules can indicate presence of a defect or the likelihood of a defect being present. A rule execution engine can search for an occurrence of the shape primitives covered by the at least one rule.

Abstract: Methods and systems for determining one or more characteristics for defects detected on a specimen are provided. One system includes one or more computer subsystems configured for identifying a first defect that was detected on a specimen by an inspection system with a first mode but was not detected with one or more other modes. The computer subsystem(s) are also configured for acquiring, from the storage medium, one or more images generated with the one or more other modes at a location on the specimen corresponding to the first defect. In addition, the computer subsystem(s) are configured for determining one or more characteristics of the acquired one or more images and determining one or more characteristics of the first defect based on the one or more characteristics of the acquired one or more images.

Abstract: Methods and systems for determining one or more characteristics for defects detected on a specimen are provided. One system includes one or more computer subsystems configured for identifying a first defect that was detected on a specimen by an inspection system with a first mode but was not detected with one or more other modes. The computer subsystem(s) are also configured for acquiring, from the storage medium, one or more images generated with the one or more other modes at a location on the specimen corresponding to the first defect. In addition, the computer subsystem(s) are configured for determining one or more characteristics of the acquired one or more images and determining one or more characteristics of the first defect based on the one or more characteristics of the acquired one or more images.

Abstract: Methods and systems for identifying outliers in multiple instances of a pattern of interest (POI) are provided. One system includes one or more computer subsystems configured for acquiring images generated by an imaging subsystem at multiple instances of a POI within a die formed on the specimen. The multiple instances include two or more instances that are located at aperiodic locations within the die. The computer subsystem(s) are also configured for determining a feature of each of the images generated at the multiple instances of the POI. In addition, the computer subsystem(s) are configured for identifying one or more outliers in the multiple instances of the POI based on the determined features.

Abstract: Criticality of a detected defect can be determined based on context codes. The context codes can be generated for a region, each of which may be part of a die. Noise levels can be used to group context codes. The context codes can be used to automatically classify a range of design contexts present on a die without needing certain information a priori.

Abstract: Methods and systems for detecting anomalies in images of a specimen are provided. One system includes one or more computer subsystems configured for acquiring images generated of a specimen by an imaging subsystem. The computer subsystem(s) are also configured for determining one or more characteristics of the acquired images. In addition, the computer subsystem(s) are configured for identifying anomalies in the images based on the one or more determined characteristics without applying a defect detection algorithm to the images or the one or more characteristics of the images.

Abstract: Shape primitives are used for inspection of a semiconductor wafer or other workpiece. The shape primitives can define local topological and geometric properties of a design. One or more rules are applied to the shape primitives. The rules can indicate presence of a defect or the likelihood of a defect being present. A rule execution engine can search for an occurrence of the shape primitives covered by the at least one rule.

Abstract: Noise induced by pattern-of-interest (POI) image registration and POI vicinity design patterns in intra-die inspection is reduced. POI are grouped into alignment groups by co-occurrence of proximate registration targets. The alignment groups are registered using the co-occurrence of proximate registration targets. Registration by voting is performed, which can measure a degree that each of the patterns-of-interest is an outlier. POI are grouped into at least one vicinity group with same vicinity design effects.