Optimization of unknown defect rejection for automatic defect classification

Patent metadata

Field	Value
Publication number	US-9715723-B2
Application number	US-201213451500-A
Country	US
Kind code	B2
Filing date	Apr 19, 2012
Priority date	Apr 19, 2012
Publication date	Jul 25, 2017
Grant date	Jul 25, 2017

Abstract

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.

First claim

What is claimed is:

1. A method for defect classification comprising:
storing a definition of a region in a feature space, wherein the definition is associated with a defect class and comprises a kernel function, wherein the kernel function comprises a kernel parameter that is associated with a plurality of parameter values, wherein each of the parameter values for the kernel parameter is used to determine a different shape of the region based on the kernel function and corresponds to a different desired rejection rate for automatic classification of a plurality of unclassified defects for the defect class;
receiving, by a processing device, a confidence threshold that corresponds to a desired rejection rate for automatic classification of at least one defect associated with the defect class;
selecting, by the processing device, one of the parameter values of the plurality of parameter values for the kernel parameter that determines the shape of the region and corresponds to the desired rejection rate associated with the confidence threshold for automatic classification of the plurality of unclassified defects;
receiving, by the processing device, inspection data for the plurality of unclassified defects detected in one or more wafer samples under inspection;
automatically classifying, by the processing device, the plurality of unclassified defects for the defect class using the kernel function and the selected one of the plurality of parameter values of the kernel parameter that determines the shape of the region and corresponds to the desired rejection rate for the plurality of unclassified defects to generate a first plurality of classification results, wherein the plurality of unclassified defects are classified with a class label associated with the region based on a location of the unclassified defects in the region with the shape determined from the kernel parameter with the one of the plurality of parameter values that is selected based on the confidence threshold that corresponds to the desired rejection rate;
obtaining a second plurality of classification results for one or more of the plurality of unclassified defects for the defect class; and
combining the first plurality of classification results and the second plurality of classification results.

2. The method of claim 1 , wherein selecting the one of the values of the kernel parameter comprises:
associating each of a plurality of confidence levels with a unique parameter value of the plurality of parameter values.

3. The method of claim 2 , wherein associating each of the plurality of confidence levels comprises:
applying the kernel function to training data to obtain a set of training results using the plurality of parameter values; and
for each of the plurality of confidence levels, assigning an optimal parameter value to the confidence level based on the training results.

4. The method of claim 3 , wherein assigning the optimal parameter value to the confidence level comprises:
selecting a parameter value for the confidence threshold to result in rejection of a subset of the plurality of defects corresponding to the confidence threshold during an automatic classification of the training data using the kernel function.

5. The method of claim 1 , wherein the definition further is of a plurality of regions in the feature space, each of the regions in the feature space is defined by a one-class support vector machine comprising the kernel function, and wherein automatically classifying the plurality of unclassified defects comprises distinguishing between a plurality of first defects belonging to the class and a plurality of second defects that are classified as unknown defects with respect to the class using the one-class support vector machine.

6. The method of claim 5 , wherein automatically classifying the plurality of unclassified defects comprises applying a multi-class support vector machine to assign each unclassified defect to a respective defect class, and identifying at least one non-decidable defect from the plurality of unclassified defects, wherein the non-decidable defect is in an overlap area between the plurality of regions of at least two of the defect classes in the feature space, and wherein the automatic classification is performed by a first inspection modality, and wherein the at least one non-decidable defect is provided to a second inspection modality to obtain the second plurality of classification results.

7. The method of claim 5 , wherein the one-class support vector machine defines a mapping of the inspection data to a hypersphere in the feature space, and wherein the confidence level and the value of the kernel parameter defines a hyperplane that divides the hypersphere to create a hyperspherical cap, wherein the hyperspherical cap is mapped to a region in feature space containing the plurality of first defects classified as belonging to the class.

8. An apparatus comprising:
a memory to store a definition of a region in a feature space, wherein the definition is associated with a defect class and comprises a kernel function, wherein the kernel function comprises a kernel parameter that is associated with a plurality of parameter values, wherein each of the parameter values for the kernel parameter is used to determine a different shape of the region based on the kernel function and corresponds to a different desired rejection rate for automatic classification of a plurality of unclassified defects for the defect class; and
a processor, operatively coupled with the memory, to:
receive a confidence threshold that corresponds to a desired rejection rate for automatic classification of at least one defect associated with the defect class;
select one of the parameter values of the plurality of parameter values for the kernel parameter that determines the shape of the region and corresponds to the desired rejection rate associated with the confidence threshold for automatic classification of the plurality of unclassified defects;
receive inspection data for the plurality of unclassified defects detected in one or more wafer samples under inspection;
automatically classify the plurality of unclassified defects for the defect class using the kernel function and the selected one of the plurality of parameter values of the kernel parameter that corresponds to the desired rejection rate for the plurality of unclassified defects to generate a first plurality of classification results;
obtain a second plurality of classification results for one or more of the plurality of unclassified defects for the class; and
combine the first plurality of classification results and the second plurality of classification results, wherein the plurality of unclassified defects are classified with a class label associated with the region based on a location of the unclassified defects in the region with the shape determined from the kernel parameter with the one of the plurality of parameter values that is selected based on the confidence threshold that corresponds to the desired rejection rate.

9. The apparatus of claim 8 , wherein the processor is to associate each of a plurality of confidence levels with a unique parameter value of the plurality of parameter values.

10. The apparatus of claim 9 , wherein to associate each of the plurality of confidence levels, the processor is to apply the kernel function to training data to obtain a set of training results using the plurality of parameter values, and for each of the plurality of confidence levels, to assign an optimal parameter value to the confidence level based on the training results.

11. The apparatus of claim 10 , wherein to assign the optimal parameter value to t

Assignees

• Shlain Vladimir
• Greenberg Gadi
• Kaizerman Idan
• Rozenman Efrat
• Applied Materials Israel Ltd

Inventors

• Shlain Vladimir
• Greenberg Gadi
• Kaizerman Idan
• Rozenman Efrat

Classifications

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/20076

  Probabilistic image processing · CPC title

• G06T2207/10061

  from scanning electron microscope · CPC title

• G06T7/0004 Primary

  Industrial image inspection · CPC title