Computer-implemented methods for performing one or more defect-related functions

• US 9,037,280 B2
• Filed: 06/06/2005
• Issued: 05/19/2015
• Est. Priority Date: 06/06/2005
• Status: Active Grant

• Current Assignee: Kla-Tencor Technologies Corporation (KLA Corporation)
• Original Assignee: Kla-Tencor Technologies Corporation (KLA Corporation)
• Inventors: Dishner, Mark, Lee, Chris W., McCauley, Sharon, Huet, Patrick, Wang, David
• Primary Examiner(s): Bahta, Kidest
• Application Number:
  US11/146,342
• Publication Number:
  US 20060287751A1
• Time in Patent Office: 3,634 Days
• Field of Search: 700108-111, 700/121, 702/35, 702/58, 702/14, 382/149, 382/146, 382/144, 356/73, 356/137.5
• US Class Current: 700/108
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods

Abstract

Computer-implemented methods for performing one or more defect-related functions are provided. One method for identifying noise in inspection data includes identifying events detected in a number of sets of inspection data that is less than a predetermined number as noise. One method for binning defects includes binning the defects into groups based on defect characteristics and the sets of the inspection data in which the defects were detected. One method for selecting defects for defect analysis includes binning defects into group(s) based on proximity of the defects to each other and spatial signatures formed by the group(s). A different method for selecting defects for defect analysis includes selecting defects having the greatest diversity of defect characteristic(s) for defect analysis. One method includes classifying defects on a specimen using inspection data generated for the specimen combined with defect review data generated for the specimen.

Claims

1. A computer-implemented method for identifying noise in inspection data, comprising:

detecting events in sets of inspection data using detection parameters known to detect noise, nuisance events, and real events, wherein the sets of inspection data are generated by different inspections performed on a specimen in a single inspection process;

identifying the events detected in a number of the sets of inspection data that is less than a predetermined number as noise;

determining one characteristic of the real events;

determining the real events having the greatest diversity of the one characteristic;

selecting the real events having the greatest diversity of the one characteristic for defect analysis; and

binning the real events into one or more groups based on proximity of the real events to each other on the specimen and spatial signatures formed by the one or more groups, wherein the binning comprises spatial signature analysis, wherein the spatial signature analysis comprises stacking the inspection data corresponding to multiple areas on the specimen, wherein the multiple areas comprise the same patterned feature design, wherein the spatial signature analysis further comprises determining the proximity of the real events to each other on the specimen and the spatial signatures formed by the one or more groups based on the stacked inspection data, and wherein said detecting, said identifying, said determining the characteristic, said determining the real events, said selecting, and said binning are performed using a computer system.

2. The method of claim 1, further comprising eliminating the events identified as noise from the sets of inspection data.

3. The method of claim 1, wherein the different inspections are performed with the same or different data acquisition conditions.

4. The method of claim 1, wherein the different inspections are performed with the same or different detection parameters.

29. The method of claim 1, wherein the single inspection process is a less than optimized inspection process.

30. The method of claim 1, wherein the single inspection process is setup without determining a set of optimal optical inspection parameters for the single inspection process.

31. The method of claim 1, wherein the detection parameters used to detect the events are not optimized.

32. The method of claim 1, wherein the different inspections use non-optimized inspection parameters.

33. The method of claim 1, further comprising determining the events detected in a number of the sets of inspection data that is greater than the predetermined number as the real events.

5. A computer-implemented method for binning defects, comprising:

determining one characteristic of defects detected in sets of inspection data generated by different inspections performed on a specimen in a single inspection process;

binning the defects into groups based on the one characteristic, the sets of the inspection data in which the defects were detected, proximity of the defects to each other on the specimen, and spatial signatures formed by the groups, wherein the binning comprises spatial signature analysis, wherein the spatial signature analysis comprises stacking the inspection data corresponding to multiple areas on the specimen, wherein the multiple areas comprise the same patterned feature design, and wherein the spatial signature analysis further comprises determining the proximity of the defects to each other on the specimen and the spatial signatures formed by the groups based on the stacked inspection data;

determining the defects having the greatest diversity of the one characteristic; and

selecting the defects having the greatest diversity of the one characteristic for defect analysis, wherein said determining the characteristic, said binning, said determining the defects, and said selecting are performed using a computer system.

6. The method of claim 5, further comprising sampling the defects in the groups for defect review.

7. The method of claim 5, wherein the different inspections are performed with the same data acquisition parameters, and wherein the defects are detected in the sets of inspection data using the same detection parameters.

8. The method of claim 5, wherein the different inspections are performed with different data acquisition parameters.

9. The method of claim 5, wherein the defects are detected in the sets of inspection data using different detection parameters.

10. The method of claim 5, wherein the different inspections are performed with different data acquisition parameters, and wherein the defects are detected in the sets of inspection data using different detection parameters.

11. The method of claim 5, wherein the defects are detected with a single set of inspection parameters, the method further comprising determining additional characteristics of the defects using data generated by an additional inspection performed on the specimen with data acquisition parameters different than data acquisition parameters of the single set.

34. The method of claim 5, wherein binning the defects into the groups based on the one characteristic, the sets of the inspection data in which the defects were detected, the proximity of the defects to each other on the specimen, and the spatial signatures formed by the groups further comprises binning the defects into the groups based on the one characteristic, information for image acquisition conditions used to produce the sets of the inspection data, the proximity of the defects to each other on the specimen, and the spatial signatures formed by the groups.

35. The method of claim 5, wherein data acquisition and processing parameters associated with the sets of inspection data are input to the binning step.

12. A computer-implemented method for selecting defects for defect analysis on an inspection system, comprising:

binning defects into one or more groups based on proximity of the defects to each other on a specimen and spatial signatures formed by the one or more groups, wherein said binning comprises spatial signature analysis, wherein the spatial signature analysis comprises stacking inspection data corresponding to multiple areas on the specimen, wherein the multiple areas comprise the same patterned feature design, and wherein the spatial signature analysis further comprises determining the proximity of the defects to each other on the specimen and the spatial signatures formed by the one or more groups based on the stacked inspection data;

determining one characteristic of the defects detected on the specimen;

determining the defects having the greatest diversity of the one characteristic;

selecting one or more of the defects in at least one of the one or more groups for defect analysis; and

selecting the defects having the greatest diversity of the one characteristic for the defect analysis, wherein said binning, said determining the characteristic, said determining the defects, said selecting the one or more of the defects, and said selecting the defects are performed using a computer system.

13. The method of claim 12, wherein said binning is performed prior to optical review on a review system.

14. The method of claim 12, wherein said binning is performed prior to electron microscope review on a microscope.

15. The method of claim 12, wherein the defect analysis comprises creating a sample for defect review.

16. The method of claim 12, wherein the defect analysis comprises defect classification.

17. The method of claim 12, wherein the defect analysis comprises creating a report on results of said binning.

18. The method of claim 12, further comprising determining how many of the defects in the one or more groups are to be selected for the defect analysis.

19. The method of claim 12, further comprising determining if any of the one or more groups is associated with a known root cause.

36. The method of claim 12, wherein the spatial signature analysis is part of an inspection recipe performed on the inspection system.

37. The method of claim 12, wherein the multiple areas are fields or dies on the specimen.

38. The method of claim 12, wherein said binning further comprises determining if the defects located in the stacked area form a signature.

39. The method of claim 12, wherein the spatial signature analysis is performed on-tool on the inspection system.

40. The method of claim 12, wherein the spatial signature analysis isolates lithographic issues that appear at edges of reticles.

41. The method of claim 12, wherein the spatial signature analysis identifies process problems that occur when density of a pattern on the specimen changes at an edge of an array.

42. The method of claim 12, wherein the spatial signature analysis is performed by a review system.

20. A computer-implemented method for selecting defects for defect analysis, comprising:

determining one characteristic of defects detected on a specimen;

determining the defects having the greatest diversity of the one characteristic;

selecting the defects having the greatest diversity of the one characteristic for defect analysis; and

binning the defects into one or more groups based on proximity of the defects to each other on the specimen and spatial signatures formed by the one or more groups, wherein the binning comprises spatial signature analysis, wherein the spatial signature analysis comprises stacking inspection data corresponding to multiple areas on the specimen, wherein the multiple areas comprise the same patterned feature design, wherein the spatial signature analysis further comprises determining the proximity of the defects to each other on the specimen and the spatial signatures formed by the one or more groups based on the stacked inspection data, and wherein said determining the characteristic, said determining the defects, said selecting, and said binning are performed using a computer system.

43. The method of claim 20, wherein the defects are detected on the specimen in a production run of a wafer inspection system.

44. The method of claim 20, wherein the computer system used to perform said determining the characteristic, said determining the defects, said selecting, and said binning is a computer system of a wafer inspection system.

45. The method of claim 20, wherein the computer system used to perform said determining the characteristic, said determining the defects, said selecting, said binning is a computer system of an optical defect review system.

46. The method of claim 20, wherein the computer system used to perform said determining the characteristic, said determining the defects, said selecting, and said binning is a computer system of a scanning electron microscope defect review system.

21. A computer-implemented method for classifying defects, comprising:

determining one characteristic of defects detected on a specimen;

determining the defects having the greatest diversity of the one characteristic;

selecting the defects having the greatest diversity of the one characteristic for defect analysis;

classifying the defects using inspection data generated for the specimen combined with defect review data generated for the specimen, wherein the inspection data comprises sets of inspection data generated by different inspections performed on the specimen in a single inspection process; and

binning the defects into one or more groups based on proximity of the defects to each other on the specimen and spatial signatures formed by the one or more groups, wherein the binning comprises spatial signature analysis, wherein the spatial signature analysis comprises stacking the inspection data corresponding to multiple areas on the specimen, wherein the multiple areas comprise the same patterned feature design, wherein the spatial signature analysis further comprises determining the proximity of the defects to each other on the specimen and the spatial signatures formed by the one or more groups based on the stacked inspection data, and wherein said determining the characteristic, said determining the defects, said selecting, said classifying, and said binning are performed using a computer system.

22. The method of claim 21, wherein the inspection data further comprises raw inspection data.

23. The method of claim 21, wherein said classifying is performed using a defect attributes based method or a feature vector based method.

24. The method of claim 21, wherein said classifying comprises automatic defect classification.

25. The method of claim 21, wherein the defect review data comprises scanning electron microscope images.

26. The method of claim 21, wherein the inspection data used for said classifying further comprises contextual information about the defects.

27. The method of claim 21, wherein the inspection data is generated with different data acquisition parameters.

28. The method of claim 21, wherein the defect review data is generated with different data acquisition parameters.