Applied Materials Israel Limited

Vladimir Shlain

Idan Kaizerman

Efrat Rozenman

Assaf Glazer

Gadi Greenberg

A method for defect classification includes storing, in a computer system, a definition of a region in a feature space. The definition is associated with a class of defects and comprises a kernel function comprising a parameter. The parameter determines a shape of the region. A confidence threshold for automatic classification of at least one defect associated with the class is received. A value of the parameter associated with the confidence threshold is selected. Inspection data for a plurality of defects detected in one or more samples under inspection is received. The plurality of defects for the class are automatically classified using the kernel function and the selected value of the parameter.

Optimization of unknown defect rejection for automatic defect classification

A method for classification includes receiving inspection data associated with a plurality of defects found in one or more samples and receiving one or more benchmark classification comprising a class for each of the plurality of defects. A readiness criterion for one or more of the classes is evaluated based on the one or more benchmark classification results, wherein the readiness criterion comprises for each class, a suitability of the inspection data for training an automatic defect classifier for the class. A portion of the inspection data is selected corresponding to one or more defects associated with one or more classes that satisfy the readiness criterion. One or more automatic classifiers are trained for the one or more classes that satisfy the readiness criterion using the selected portion of the inspection data.

Classifier readiness and maintenance in automatic defect classification

A method for defect analysis includes identifying single-class classifiers for a plurality of defect classes, the plurality of defect classes characterized by respective ranges of inspection parameter values. Each single-class classifier is configured for a respective class to identify defects belonging to the respective class based on the inspection parameter values, while identifying the defects not in the respective class as unknown defects. A multi-class classifier is identified that is configured to assign each defect to one of the plurality of the defect classes based on the inspection parameter values. Inspection data is received, and both the single-class and multi-class classifiers are applied to the inspection data to assign the defect to one of the defect classes.

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Haifa, Israel

Vladimir Shlain