METHODS AND APPARATUS FOR DATA ANALYSIS

• US 20110178967A1
• Filed: 03/09/2011
• Published: 07/21/2011
• Est. Priority Date: 05/24/2001
• Status: Abandoned Application

• Current Assignee: In-Depth Test LLC (Acacia Research Corporation)
• Original Assignee: Test Advantage Incorporated (Boston Semi Equipment, LLC)
• Inventors: Delp, Deana
• Application Number:
  US13/044,202
• Publication Number:
  US 20110178967A1
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G01R 31/2894 Aspects of quality control [QC] G01R31/31718 takes precedence; program control for QC G05B19/41875
• 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 23/0278 Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
• CPC Class Codes: G06F 11/2263 using neural networks
• CPC Class Codes: G06F 11/263 Generation of test inputs, e.g. test vectors, patterns or sequences ; with adaptation of the tested hardware for testability with external testers
• CPC Class Codes: G06F 11/273 Tester hardware, i.e. output processing circuits G06F11/263 takes precedence
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

A method and apparatus for data analysis according to various aspects of the present invention is configured to test a set of components and generate test data for the components. A diagnostic system automatically analyzes the test data to identify a characteristic of a component fabrication process by recognizing a pattern in the test data and classifying the pattern using a neural network.

Claims

1. A test system, comprising:

a tester configured to test a set of components and generate test data for the set of components;

a diagnostic system configured to receive the test data from the tester, automatically analyze the test data to identify a characteristic in a process for fabricating the components, and recognize a pattern in the test data, wherein the diagnostic system comprises a classifier configured to classify the pattern using a neural network.

2. A test system according to claim 1, wherein the neural network comprises a self-organizing map.

3. A test system according to claim 1, wherein the diagnostic system comprises at least two stages, wherein:

a first stage comprises a plurality of classifiers configured to receive different types of test data and generates first stage data based on the different types of test data; and

a second stage configured to receive the first stage data from the plurality of classifiers and classify the pattern using the neural network based on the first stage data.

4. A test system according to claim 3, wherein the first and second stages comprise self-learning systems, and the first and second stages are independently trainable.

5. A test system according to claim 1, further comprising a neural network library, wherein the diagnostic system is configured to retrieve a stored neural network from the neural network library to analyze the test data.

6. A test system according to claim 5, wherein the diagnostic system is configured to select the neural network from the neural network library based on at least one of the test data'"'"'s data population type and the type of test generated by the tester.

7. A test system according to claim 1, wherein the neural network is self-learning.

8. A test system according to claim 7, wherein the self-learning system is configured to generate a class from the test data generated by the tester.

9. A test system according to claim 1, wherein the diagnostic system is configured to generate supplemental data based on the test data, wherein the supplemental data is not dependent on a type of the components or a type of the test data.

10. A test system according to claim 1, wherein the diagnostic system further comprises a second neural network configured to filter the test data.

11. A test system according to claim 10, wherein the second neural network comprises a self-organizing map.

12. A test system according to claim 1, wherein the diagnostic system is configured to identify a trend in the test data.

13. 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 comprising a pattern recognition system having access to the memory and configured to classify patterns in the test data using a neural network and identify a characteristic of the fabrication process based on the test data.

14. A test data analysis system according to claim 13, wherein the neural network comprises a self-organizing map.

15. A test data analysis system according to claim 13, wherein the diagnostic system comprises at least two stages, wherein:

a first stage comprises a plurality of classifiers configured to receive different types of test data and generates first stage data based on the different types of test data; and

a second stage configured to receive the first stage data from the plurality of classifiers and classify the pattern using the neural network based on the first stage data.

16. A test data analysis system according to claim 15, wherein the neural network comprises a self-organizing map.

17. A test data analysis system according to claim 15, wherein the first and second stages comprise self-learning systems, and the first and second stages are independently trainable.

18. A test data analysis system according to claim 13, further comprising a neural network library, wherein the diagnostic system is configured to retrieve a stored neural network from the neural network library to analyze the test data.

19. A test data analysis system according to claim 13, wherein the pattern recognition system comprises a classifier configured to classify the recognized pattern according to a known pattern.

20. A test data analysis system according to claim 19, wherein the pattern recognition system comprises a feature extractor configured to extract a feature from the test data associated with the pattern.

21. A test data analysis system according to claim 20, 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.

22. A test system analysis system according to claim 13, wherein the diagnostic system further comprises a second neural network configured to filter the test data.

23. A test system analysis system according to claim 22, wherein the second neural network comprises a self-organizing map.

28. A computer-implemented method for testing components according to claim 22, wherein automatically identifying the characteristic comprises extracting a feature from the test data associated with a recognized pattern.

29. A computer-implemented method for testing components according to claim 28, wherein automatically identifying the characteristic further comprises selecting the feature from multiple features for analysis.

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

obtaining test data for the components; and

analyzing the test data and automatically identifying a characteristic of the fabrication process based on the test data wherein automatically identifying the characteristic comprises classifying a pattern in the test data using a neural network.

25. A test data analysis system according to claim 24, wherein the neural network comprises a self-organizing map.

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

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

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; and

analyzing the test data and automatically identifying a characteristic of the fabrication process based on the test data comprising;

recognizing a pattern in the test data;

comparing the recognized pattern to a known pattern associated with the characteristic; and

classifying the recognized pattern according to a known pattern using a neural network.

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, wherein the neural network comprises a self-organizing map.

33. A medium storing instructions according to claim 30, wherein automatically identifying the characteristic comprises extracting a feature from the test data associated with the recognized pattern.

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