OPTIMIZATION OF UNKNOWN DEFECT REJECTION FOR AUTOMATIC DEFECT CLASSIFICATION

• US 20130279795A1
• Filed: 04/19/2012
• Published: 10/24/2013
• Est. Priority Date: 04/19/2012
• Status: Active Grant

• Current Assignee: Applied Materials Israel Limited (Applied Materials Incorporated)
• Original Assignee: Applied Materials Israel Limited (Applied Materials Incorporated)
• Inventors: Shlain, Vladimir, Greenberg, Gadi, Kaizerman, Idan, Rozenman, Efrat
• Granted Patent: US 9,715,723 B2
• Time in Patent Office: Days
• US Class Current: 382/149
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06T 2207/10061 from scanning electron microscope
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/987 with the intervention of an operator

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.

Claims

1. A method for defect classification comprising:

storing, in a computer system, a definition of a region in a feature space, wherein the definition is associated with a class of defects and comprises a kernel function comprising a parameter, wherein the parameter determines a shape of the region;

receiving, by the computer system, a confidence threshold for automatic classification of at least one defect associated with the class;

selecting, by the computer system, a value of the parameter associated with the confidence threshold;

receiving, by the computer system, inspection data for a plurality of defects detected in one or more samples under inspection; and

automatically classifying, by the computer system, the plurality of defects for the class using the kernel function and the selected value of the parameter.

2. The method of claim 1, wherein selecting the value comprises:

associating each of a plurality of confidence levels with a unique parameter value of a plurality of parameter values; and

selecting the parameter value that is associated with the confidence threshold.

3. The method of claim 2, wherein associating 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

assigning an optimal parameter value to each confidence level based on the training results.

4. The method of claim 3, wherein assigning the optimal parameter value to each confidence level comprises:

selecting a parameter value for each 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 region in the feature space is defined by a one-class support vector machine comprising the kernel function, and wherein automatically classifying the plurality of 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 defects comprises applying a multi-class support vector machine to assign each defect to a respective defect class, and identifying at least one non-decidable defect, wherein the non-decidable defect is in an overlap area between a plurality of regions of at least two of the defect classes in the feature space.

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 parameter define a hyperplane that divides the hypersphere to create a hyperspherical cap, wherein the hyperscherical cap is mapped to a region in feature space containing the plurality of defects classified as belonging to the class.

8. An apparatus comprising:

a memory configured to store a definition of a region in a feature space, wherein the definition is associated with a class of defects and comprises a kernel function comprising a parameter, wherein the parameter determines a shape of the region; and

a processor configured to receive a confidence threshold for automatic classification of at least one defect associated with the class, to select a value of the parameter associated with the confidence threshold, to receive inspection data for a plurality of defects detected in one or more samples under inspection, and to automatically classify the plurality of defects for the class using the kernel function with the selected value of the parameter.

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

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

11. The apparatus of claim 10, wherein the processor is configured to select a parameter value for each 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.

12. The apparatus of claim 8, wherein the region in the feature space is defined by a one-class support vector machine comprising the kernel function, and wherein automatically classifying the plurality of 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.

13. The apparatus of claim 12, wherein the processor is configured to apply a multi-class support vector machine to assign each defect to a respective defect class, and identifying at least one non-decidable defect, wherein the non-decidable defect is in an overlap area between a plurality of regions of at least two of the defect classes in the feature space.

14. The apparatus of claim 12, 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 parameter define a hyperplane that divides the hypersphere to create a hyperspherical cap, wherein the hyperscherical cap is mapped to a region in feature space containing the plurality of defects classified as belonging to the class.

15. A non-transitory computer readable storage medium having instructions that, when executed by a processing device, cause the processing device to perform operations comprising:

storing a definition of a region in a feature space, wherein the definition is associated with a class of defects and comprises a kernel function comprising a parameter, wherein the parameter determines a shape of the region;

receiving a confidence threshold for automatic classification of at least one defect associated with the class;

selecting a value of the parameter associated with the confidence threshold;

receiving inspection data for a plurality of defects detected in one or more samples under inspection; and

automatically classifying the plurality of defects for the class using the kernel function and the selected value of the parameter.

16. The non-transitory computer readable storage medium of claim 15, wherein the processing device further performs operations comprising:

associating each of a plurality of confidence levels with a unique parameter value of a plurality of parameter values; and

selecting the parameter value that is associated with the confidence threshold.

17. The non-transitory computer readable storage medium of claim 16, wherein the processing device further performs operations comprising:

applying the kernel function to training data to obtain a set of training results using the plurality of parameter values; and

assigning an optimal parameter value to each confidence level based on the training results.

18. The non-transitory computer readable storage medium of claim 17, wherein the processing device further performs operations comprising:

selecting a parameter value for each 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.

19. The non-transitory computer readable storage medium of claim 15, wherein the region in the feature space is defined by a one-class support vector machine comprising the kernel function, and wherein automatically classifying the plurality of 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.

20. The non-transitory computer readable storage medium of claim 19, wherein processing device further performs operations comprising:

applying a multi-class support vector machine to assign each defect to a respective defect class, and identifying at least one non-decidable defect, wherein the non-decidable defect is in an overlap area between a plurality of regions of at least two of the defect classes in the feature space.

21. The non-transitory computer readable storage medium of claim 19, 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 parameter define a hyperplane that divides the hypersphere to create a hyperspherical cap, wherein the hyperscherical cap is mapped to a region in feature space containing the plurality of defects classified as belonging to the class.