METHOD AND SYSTEM FOR CREATING KNOWLEDGE AND SELECTING FEATURES IN A SEMICONDUCTOR DEVICE

• US 20080175468A1
• Filed: 01/24/2007
• Published: 07/24/2008
• Est. Priority Date: 01/24/2007
• Status: Abandoned Application

• Current Assignee: Hermes Microvision, Inc. (ASML Holding NV)
• Original Assignee: Hermes Microvision, Inc. (ASML Holding NV)
• Inventors: Zhang, Zhaoli, Jau, Jack Y., Fang, Wei
• Application Number:
  US11/626,795
• Publication Number:
  US 20080175468A1
• Time in Patent Office: Days
• US Class Current: 382/149
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

A method and system for creating knowledge and selecting features in a supervised classifier is disclosed. The method and system comprises changing a feature space of a plurality of defects and marking at least a portion of the samples of the defects in the feature space. The method and system includes labeling the at least a portion of the samples as training samples, determining if the training samples are of the same type and creating knowledge based upon the training samples if the samples are of the same type.

Claims

1. A method for creating knowledge and selecting features for a supervised classifier comprising:

changing a feature space of a plurality of defects;

marking at least a portion of the samples of the defects in the feature space;

labeling the at least a portion of the samples as training samples;

determining if the training samples are of the same type; and

creating knowledge based upon the training samples if the samples are of the same type.

2. The method of claim 1 wherein changing the feature space comprises:

randomly selecting and labeling some samples as a set; and

changing the feature space view of the set.

3. The method of claim 1 wherein features are selected related to the defect utilizing the visual classifier, wherein a one dimensional visual classifier element, a two dimension visual classifier element and a three dimensional visual classifier element are utilized sequentially to select the best feature.

4. A method for creating knowledge and selecting features for a supervised classifier comprising:

changing a feature space of a plurality of defects;

marking at least a portion of the samples of the defects in the feature space;

labeling the at least a portion of the samples as a T set element;

changing the feature space of the T set element;

deciding upon appropriate parameters;

determining if the parameters can be used to classify the T set element; and

creating knowledge based upon the parameters if the parameters are used.

5. The method of claim 4 wherein changing the feature space comprises:

randomly selecting and labeling some samples as a set; and

changing the feature space view of the set.

6. The method of claim 4 wherein features are selected related to the defect utilizing the visual classifier, wherein a one dimensional visual classifier element, a two dimension visual classifier element and a three dimensional visual classifier element are utilized sequentially to select the best feature.

7. A computer readable medium for creating knowledge and selecting features for a supervised classifier comprising:

changing a feature space of a plurality of defects;

marking at least a portion of the samples of the defects in the feature space;

labeling the at least a portion of the samples as training samples;

determining if the training samples are of the same type; and

creating knowledge based upon the training samples if the samples are of the same type.

8. The computer readable medium of claim 7 wherein changing the feature space comprises:

randomly selecting and labeling some samples as a set; and

changing the feature space view of the set.

9. The computer readable medium of claim 7 wherein features are selected related to the defect utilizing the visual classifier, wherein a one dimensional visual classifier element, a two dimension visual classifier element and a three dimensional visual classifier element are utilized sequentially to select the best feature.

10. A method for creating knowledge and selecting features for a supervised classifier comprising:

changing a feature space of a plurality of defects;

marking at least a portion of the samples of the defects in the feature space;

labeling the at least a portion of the samples as a T set element;

changing the feature space of the T set element;

deciding upon appropriate parameters;

determining if the parameters can be used to classify the T set element; and

creating knowledge based upon the parameters if the parameters are used.

11. The method of claim 10 wherein changing the feature space comprises:

randomly selecting and labeling some samples as a set; and

changing the feature space view of the set.

12. The method of claim 10 wherein features are selected related to the defect utilizing the visual classifier, wherein a one dimensional visual classifier element, a two dimension visual classifier element and a three dimensional visual classifier element are utilized sequentially to select the best feature.