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:
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:
Methods and systems for determining characteristic(s) of patterns of interest (POIs) are provided. One system is configured to acquire output of an inspection system generated at the POI instances without detecting defects at the POI instances. The output is then used to generate a selection of the POI instances. The system then acquires output from an output acquisition subsystem for the selected POI instances. The system also determines characteristic(s) of the POI using the output acquired from the output acquisition subsystem.
Abstract:
Systems and methods for detecting defects on a wafer are provided. One method includes determining locations of all instances of a weak geometry in a design for a wafer. The locations include random, aperiodic locations. The weak geometry includes one or more features that are more prone to defects than other features in the design. The method also includes scanning the wafer with a wafer inspection system to thereby generate output for the wafer with one or more detectors of the wafer inspection system. In addition, the method includes detecting defects in at least one instance of the weak geometry based on the output generated at two or more instances of the weak geometry in a single die on the wafer.
Abstract:
Methods and systems for determining characteristic(s) of patterns of interest (POIs) are provided. One system is configured to acquire output of an inspection system generated at the POI instances without detecting defects at the POI instances. The output is then used to generate a selection of the POI instances. The system then acquires output from an output acquisition subsystem for the selected POI instances. The system also determines characteristic(s) of the POI using the output acquired from the output acquisition subsystem.
Abstract:
Methods and systems for generating simulated output for a specimen are provided. One method includes acquiring information for a specimen with one or more computer systems. The information includes at least one of an actual optical image of the specimen, an actual electron beam image of the specimen, and design data for the specimen. The method also includes inputting the information for the specimen into a learning based model. The learning based model is included in one or more components executed by the one or more computer systems. The learning based model is configured for mapping a triangular relationship between optical images, electron beam images, and design data, and the learning based model applies the triangular relationship to the input to thereby generate simulated images for the specimen.
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:
Systems and methods for detecting defects on a wafer are provided. One method includes determining locations of all instances of a weak geometry in a design for a wafer. The locations include random, aperiodic locations. The weak geometry includes one or more features that are more prone to defects than other features in the design. The method also includes scanning the wafer with a wafer inspection system to thereby generate output for the wafer with one or more detectors of the wafer inspection system. In addition, the method includes detecting detects in at least one instance of the weak geometry based on the output generated at two or more instances of the weak geometry in a single die on the wafer.