Creating defect classifiers and nuisance filters
First Claim
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1. A method for setting up a classifier for defects detected on a wafer, comprising:
- generating a template for a defect classifier for defects detected on a wafer;
applying the template to a training data set, wherein the training data set comprises information for defects detected on the wafer or another water, wherein applying the template to the training data set automatically creates an initial version of the defect classifier, and wherein the training data set does not comprise classifications for defects in the training data set; and
determining one or more parameters for the defect classifier based on results of said applying, wherein determining the one or more parameters comprises tuning one or more parameters of the initial version of the defect classifier to determine the one or more parameters for the defect classifier, and wherein the generating, applying, and determining steps are performed with a computer system.
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Abstract
Methods and systems for setting up a classifier for defects detected on a wafer are provided. One method includes generating a template for a defect classifier for defects detected on a wafer and applying the template to a training data set. The training data set includes information for defects detected on the wafer or another wafer. The method also includes determining one or more parameters for the defect classifier based on results of the applying step.
31 Citations
41 Claims
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1. A method for setting up a classifier for defects detected on a wafer, comprising:
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generating a template for a defect classifier for defects detected on a wafer; applying the template to a training data set, wherein the training data set comprises information for defects detected on the wafer or another water, wherein applying the template to the training data set automatically creates an initial version of the defect classifier, and wherein the training data set does not comprise classifications for defects in the training data set; and determining one or more parameters for the defect classifier based on results of said applying, wherein determining the one or more parameters comprises tuning one or more parameters of the initial version of the defect classifier to determine the one or more parameters for the defect classifier, and wherein the generating, applying, and determining steps are performed with a computer system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A non-transitory computer-readable medium, storing program instructions executable on a computer system for performing a computer-implemented method for setting up a classifier for defects detected on a wafer, wherein the computer-implemented method comprises:
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generating a template for a defect classifier for defects detected on a wafer; applying the template to a training data set, wherein the training data set comprises information for defects detected on the wafer or another wafer, wherein applying the template to the training data set automatically creates an initial version of the defect classifier, and wherein the training data set does not comprise classifications for defects in the training data set; and determining one or more parameters for the defect classifier based on results of said applying, wherein determining the one or more parameters comprises tuning one or more parameters of the initial version of the defect classifier to determine the one or more parameters for the defect classifier.
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22. A system configured to set up a classifier for defects detected on a wafer, comprising a computer system configured for:
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generating a template for a defect classifier for defects detected on a wafer; applying the template to a training data set, wherein the training data set comprises information for defects detected on the wafer or another wafer, wherein applying the template to the training data set automatically creates an initial version of the defect classifier, and wherein the training data set does not comprise classifications for defects in the training data set; and determining one or more parameters for the defect classifier based on results of said applying, wherein determining the one or more parameters comprises tuning one or more parameters of the initial version of the defect classifier to determine the one or more parameters for the defect classifier. - View Dependent Claims (23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41)
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Specification