Methods and apparatus for data analysis

• US 7,225,107 B2
• Filed: 12/07/2003
• Issued: 05/29/2007
• Est. Priority Date: 05/24/2001
• Status: Expired due to Term

• Current Assignee: In-Depth Test LLC (Acacia Research Corporation)
• Original Assignee: Test Advantage Incorporated (Boston Semi Equipment, LLC)
• Inventors: Martin, Emilio Miguelanez, Tabor, Eric Paul, Buxton, Paul M., Zalzala, Ali M. S.
• Primary Examiner(s): Barlow, Jr.; John E.
• Assistant Examiner(s): KHUU, HIEN DIEU THI
• Application Number:
  US10/730,388
• Publication Number:
  US 20040138846A1
• Time in Patent Office: 1,269 Days
• Field of Search: 702/183, 702/118, 702/182, 702/185, 702/108, 702/117
• US Class Current: 702/183
• CPC Class Codes: G05B 2219/1112 Bit addressing, handling
• CPC Class Codes: G05B 2223/02 Indirect monitoring, e.g. monitoring production to detect faults of a system
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms
• CPC Class Codes: G05B 23/0294 Optimizing process, e.g. process efficiency, product quality
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection

Abstract

A method and apparatus for data analysis according to various aspects of the present invention is configured to automatically identify a characteristic of a fabrication process for components based on test data for the components.

Claims

1. A test system, comprising:

a tester configured to test a set of components and generate test data for the set of components, wherein the components are fabricated in accordance with a fabrication process; and

a diagnostic system configured to receive the test data from the tester and automatically analyze the test data to identify a characteristic of the fabrication process for the components, wherein the diagnostic system comprises a pattern recognition system configured to recognize a pattern in the test data, wherein the pattern recognition system includes a feature extractor configured to extract a feature from the test data associated with the pattern, wherein the feature extractor calculates at least one of a mass, a centroid, a geometric moment, and a moment of Hu based on the test data.

2. A test system according to claim 1, wherein the test data comprises at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and outlier data.

3. A test system according to claim 1, wherein the diagnostic system is configured to provide a corrective action suggestion based on the identified characteristic.

4. A test system according to claim 1, wherein the pattern recognition system is configured to compare the recognized pattern to a known pattern associated with the characteristic.

5. A test system according to claim 1, wherein the pattern recognition system comprises an intelligent system configured to automatically learn an additional pattern based on the recognized pattern.

6. A test system according to claim 1, wherein the pattern recognition system comprises a classifier configured to classify the recognized pattern according to a known pattern.

7. A test system according to claim 6, wherein the classifier comprises a neural network.

8. A test system according to claim 7, wherein the neural network comprises a radial basis function network.

9. A test system according to claim 1, wherein the feature extractor is configured to extract at least two features from the test data, and wherein the pattern recognition system further comprises a feature selector configured to select fewer than all of the features for analysis.

10. A test system according to claim 9, wherein the feature selector operates in conjunction with a genetic algorithm.

11. A test data analysis system for analyzing test data for a set of components fabricated and tested using a fabrication process, comprising:

a memory for storing the test data; and

a diagnostic system having access to the memory and configured to identify a characteristic of the fabrication process based on the test data, wherein the diagnostic system comprises a pattern recognition system configured to recognize a pattern in the test data, wherein the pattern recognition system includes a feature extractor configured to extract a feature from the test data associated with the pattern, wherein the feature extractor calculates at least one of a mass, a centroid, a geometric moment, and a moment of Hu based on the test data.

12. A test data analysis system according to claim 11, wherein the test data comprises at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and outlier data.

13. A test data analysis system according to claim 11, wherein the diagnostic system is configured to provide a corrective action suggestion based on the identified characteristic.

14. A test data analysis system according to claim 11, wherein the pattern recognition system is configured to compare the recognized pattern to a known pattern associated with the characteristic.

15. A test data analysis system according to claim 11, wherein the pattern recognition system comprises an intelligent system configured to automatically learn an additional pattern based on the recognized pattern.

16. A test data analysis system according to claim 11, wherein the pattern recognition system comprises a classifier configured to classify the recognized pattern according to a known pattern.

17. A test data analysis system according to claim 16, wherein the classifier comprises a neural network.

18. A test data analysis system according to claim 17, wherein the neural network comprises a radial basis function network.

19. A test data analysis system according to claim 11, wherein the feature extractor is configured to extract at least two features from the test data, and wherein the pattern recognition system further comprises a feature selector configured to select fewer than all of the features for analysis.

20. A test data analysis system according to claim 19, wherein the feature selector operates in conjunction with a genetic algorithm.

21. A computer-implemented method for testing components fabricated and tested according to a fabrication process, comprising:

obtaining test data for the components;

automatically identifying a characteristic of the fabrication process based on the test data, wherein automatically identifying the characteristic comprises recognizing a pattern in the test data, wherein antomaticatly identifying the characteristic comprises extracting a feature from the test data associated with the recognized pattern, wherein the feature comprises at least one of a mass, a centroid, a geometric moment, and a moment of Hu based on the test data; and

storing information relating to the identified characteristic in a memory.

22. A computer-implemented method for testing components according to claim 21, wherein the test data comprises at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and outlier data.

23. A computer-implemented method for testing components according to claim 21, further comprising providing a corrective action suggestion based on the identified characteristic.

24. A computer-implemented method for testing components according to claim 21, wherein automatically identifying the characteristic further comprises comparing the recognized pattern to a known pattern associated with the characteristic.

25. A computer-implemented method for testing components according to claim 21, further comprising automatically learning an additional pattern based on the recognized pattern.

26. A computer-implemented method for testing components according to claim 21, wherein automatically identifying the characteristic comprises classifying the recognized pattern according to a known pattern.

27. A computer-implemented method for testing components according to claim 26, wherein classifying the recognized pattern is performed by a neural network.

28. A computer-implemented method for testing components according to claim 27, wherein the neural network comprises a radial hasis function network.

29. A computer-implemented method for testing components according to claim 21, wherein automatically identifying the characteristic further comprises selecting the feature from multiple features for analysis.

30. A medium storing instructions executable by a machine, wherein the instructions cause the machine to execute a method for analyzing test data comprising:

obtaining test data for the components;

automatically identifying a characteristic of the fabrication process based on the test data, wherein automatically identifying the characteristic comprises recognizing a pattern in the test data, wherein automatically identifying the characteristic comprises extracting a feature from the test data associated with the recognized pattern, wherein the feature comprises at least one of a mass, a centroid, a geometric moment, and a moment of Hu based on the test data; and

storing information relating to the identified characteristic in a memory.

31. A medium storing instructions according to claim 30, wherein the test data comprises at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and outlier data.

32. A medium storing instructions according to claim 30, the method for analyzing further comprising providing a corrective action suggestion based on the identified characteristic.

33. A medium storing instructions according to claim 30, wherein automatically identifying the characteristic further comprises comparing the recognized pattern to a known pattern associated with the characteristic.

34. A medium storing instructions according to claim 30, the method for analyzing further comprising automatically learning an additional pattern based on the recognized pattern.

35. A medium storing instructions according to claim 30, wherein automatically identifying the characteristic comprises classifying the recognized pattern according to a known pattern.

36. A medium storing instructions according to claim 35, wherein classifying the recognized pattern is performed by a neural network.

37. A medium storing instructions according to claim 36, wherein the neural network comprises a radial basis function network.

38. A medium storing instructions according to claim 30, wherein automatically identifying the characteristic further comprises selecting the feature from multiple features for analysis.