Methods and apparatus for data analysis
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.
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, automatically analyze the test data, and identify a characteristic of the fabrication process for the components according to the analysis, wherein the diagnostic system comprises a pattern recognition system configured to:
recognize a pattern in the test data;
compare the recognized pattern to a known pattern associated with the characteristic; and
automatically learn an additional pattern associated with a new characteristic based on the recognized pattern, wherein automatically learn the additional pattern associated with the new characteristic does not require the pattern recognition system to retrain.
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 comprises a classifier configured to classify the recognized pattern according to a known pattern.
5. A test system according to claim 4 , wherein the classifier comprises a neural network.
6. A test system according to claim 5 , wherein the neural network comprises a radial basis function network.
7. A test system according to claim 1 , wherein the pattern recognition system includes a feature extractor configured to extract a feature from the test data associated with the pattern.
8. A test system according to claim 7 , 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.
9. A test system according to claim 7 , 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 analyze the test data, and 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;
compare the recognized pattern to a known pattern associated with the characteristic; and
automatically learn an additional pattern associated with a new characteristic based on the recognized pattern, wherein automatically learn the additional pattern associated with the new characteristic does not require the pattern recognition system to retrain.
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 comprises a classifier configured to classify the recognized pattern according to a known pattern.
15. A test data analysis system according to claim 14 , wherein the classifier comprises a neural network.
16. A test data analysis system according to claim 15 , wherein the neural network comprises a radial basis function network.
17. A test data analysis system according to claim 11 , wherein the pattern recognition system includes a feature extractor configured to extract a feature from the test data associated with the pattern.
18. A test data analysis system according to claim 17 , 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.
19. A test data analysis system according to claim 17 , 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 from a computer memory; and
analyzing the test data and automatically identifying a characteristic of the fabrication process based on the test data by the computer, wherein automatically identifying the characteristic comprises:
recognizing a pattern in the test data;
comparing the recognized pattern to a known pattern associated with the characteristic; and
automatically learning an additional pattern associated with a new characteristic based on the recognized pattern, wherein automatically learning the additional pattern associated with the new characteristic does not require retraining.
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 comprises classifying the recognized pattern according to a known pattern.
25. A computer-implemented method for testing components according to claim 24 , wherein classifying the recognized pattern is performed by a neural network.
26. A computer-implemented method for testing components according to claim 25 , wherein the neural network comprises a radial basis function network.
27. A computer-implemented method for testing components according to claim 21 , wherein automatically identifying the characteristic comprises extracting a feature from the test data associated with the recognized pattern.
28. A computer-implemented method for testing components according to claim 27 , 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.
29. A computer-implemented method for testing components according to claim 27 , wherein automatically identifying the characteristic further comprises selecting the feature from multiple features for analysis.
30. A non-transitory computer readable medium containing computer instructions stored therein for causing a computer processor to perform a method for analyzing test data comprising:
obtaining test data for a set of 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:
recognizing a pattern in the test data;
comparing the recognized pattern to a known pattern associated with the characteristic; and
automatically learning an additional pattern associated with a new characteristic based on the recognized pattern, wherein automatically learning the additional pattern associated with the new characteristic does not require retraining.
31. A non-transitory computer readable medium 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 non-transitory computer readable medium according to claim 30 , the method for analyzing further comprising providing a corrective action suggestion based on the identified characteristic.
33. A non-transitory computer readable medium according to claim 30 , wherein automatically identifying the characteristic comprises classifying the recognized pattern according to a known pattern.
34. A non-transitory computer readable medium according to claim 33 , wherein classifying the recognized pattern is performed by a neural network.
35. A non-transitory computer readable medium according to claim 34 , wherein the neural network comprises a radial basis function network.
36. A non-transitory computer readable medium according to claim 30 , wherein automatically identifying the characteristic comprises extracting a feature from the test data associated with the recognized pattern.
37. A non-transitory computer readable medium according to claim 36 , wherein the feature comprises at least one of a mass, a centroid, a geometric moment, and a moment of Flu based on the test data.
38. A non-transitory computer readable medium according to claim. 36 , wherein automatically identifying the characteristic further comprises selecting the feature from multiple features for analysis.