Systems and methods for region-adaptive defect detection

• US 10,535,131 B2
• Filed: 11/14/2016
• Issued: 01/14/2020
• Est. Priority Date: 11/18/2015
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation
• Original Assignee: KLA-Tencor Corporation
• Inventors: Maher, Christopher, Brauer, Bjorn, Ramachandran, Vijayakumar, Karsenti, Laurent, Rosengaus, Eliezer, Jordan, III, John R., Miller, Roni
• Primary Examiner(s): Perlman, David
• Application Number:
  US15/350,632
• Publication Number:
  US 20170140516A1
• Time in Patent Office: 1,156 Days
• Field of Search: None
• CPC Class Codes: G06T 2207/20182 Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/001 using an image reference approach
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 10/751 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 10/758 Involving statistics of pixels or of feature values, e.g. histogram matching

Abstract

A defect detection method includes acquiring a reference image; selecting a target region of the reference image; identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region; acquiring a test image; masking the test image with the target region of the reference image and the one or more comparative regions of the reference image; defining a defect threshold for the target region in the test image based on the one or more comparative regions in the test image; and determining whether the target region of the test image contains a defect based on the defect threshold.

Claims

1. A defect detection method comprising:

acquiring a reference image;

selecting a target region of the reference image;

identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region;

acquiring a test image;

masking the test image with the target region of the reference image and the one or more comparative regions of the reference image to generate a masked test image;

defining a defect threshold based on a pixel value distribution including pixel values for pixels within the one or more comparative regions of the test image; and

determining whether the test image contains a defect by comparing a distribution of pixel values in the masked test image to the defect threshold.

2. The defect detection method of claim 1, wherein the matching metric includes a selected range of pixel values, wherein identifying the one or more comparative regions of the reference image comprises:

identifying one or more regions of the reference image having pixel values within the selected range.

3. The defect detection method of claim 2, wherein the pixel values include grayscale values.

4. The defect detection method of claim 2, wherein the pixel value distribution includes pixel values for pixels within the target region and the one or more comparative regions in the test image.

5. The defect detection method of claim 4, wherein the pixel value distribution includes a histogram of the pixel values for the pixels within the target region and the one or more comparative regions in the test image.

6. The defect detection method of claim 4, wherein defining the defect threshold for the target region based on the pixel distribution comprises:

defining the defect threshold for the target region to identify outliers associated with a tail of the pixel value distribution.

7. The defect detection method of claim 4, wherein determining whether the test image contains a defect by comparing a distribution of pixel values in the masked test image to the defect threshold comprises:

determining whether the target region or any of the one or more comparative regions of the test image contains a defect based on the defect threshold.

8. The defect detection method of claim 1, wherein the target region includes a single target pixel.

9. The defect detection method of claim 8, wherein identifying the one or more comparative regions of the reference image comprises:

defining a vicinity pattern including a defined layout of pixels;

defining a target vicinity in the reference image arranged according to the vicinity pattern, wherein the target vicinity includes the target pixel, wherein the matching metric includes a pixel value distribution of the target vicinity; and

identifying one or more comparative vicinities of the reference image arranged according to the vicinity pattern based on the matching metric, wherein the one or more comparative regions include the one or more comparative vicinities.

10. The defect detection method of claim 9, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference image based on locality-sensitive hatching.

11. The defect detection method of claim 9, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference image based on kernalized locality-sensitive hatching.

12. The defect detection method of claim 9, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference images based on a pattern-matching technique.

13. The defect detection method of claim 9, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference images based on design data.

14. The defect detection method of claim 9, further comprising:

storing one or more locations of the one or more comparative vicinities in a database.

15. The defect detection method of claim 14, wherein the database comprises:

an indexed database.

16. The defect detection method of claim 9, wherein defining the defect threshold for the target region in the test image based on the one or more comparative regions in the test image comprises:

calculating one or more pixel value distributions of the one or more comparative vicinities of the test image;

estimating a pixel value distribution in the target vicinity of the test image based on the pixel value distributions of the one or more comparative vicinities of the test image; and

defining the defect threshold for the target pixel based on the estimated pixel value distribution in the target vicinity.

17. The defect detection method of claim 16, wherein determining whether the test image contains a defect comprises:

determining whether the target region of the test image contains a defect based on a comparison of the estimated pixel value distribution and an actual pixel value distribution of the target vicinity in the test image.

18. The defect detection method of claim 9, wherein a size of the vicinity pattern is based on an interaction function of a measurement tool used to generate the target image.

19. The defect detection method of claim 9, wherein a size of the vicinity pattern is based on a resolution of a measurement tool used to generate the target image.

20. The defect detection method of claim 1, wherein reference data comprises:

at least one of a reference image or design data.

21. The defect detection method of claim 20, wherein the reference image is based on a golden die.

22. The defect detection method of claim 20, wherein the reference image is based on at least one reference die.

23. The defect detection method of claim 20, wherein the reference image comprises:

a median reference die.

24. The defect detection method of claim 20, wherein the design data comprises:

at least one of netlist data or a design layout of one or more structures.

25. A defect detection system comprising:

an inspection sub-system comprising;

an illumination source configured to generate a beam of illumination;

a set of illumination optics to direct the beam of illumination to a sample; and

a detector configured to collect illumination emanating from the sample; and

a controller communicatively coupled to the detector, the controller including a memory device and one or more processors configured to execute program instructions configured to cause the one or more processors to;

acquire a reference image;

select a target region of the reference image;

identify, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region;

acquire a test image;

mask the test image with the target region of the reference image and the one or more comparative regions of the reference image to generate a masked test image;

define a defect threshold based on a pixel value distribution including pixel values for pixels within the one or more comparative regions of the test image; and

determine whether the test image contains a defect by comparing a distribution of pixel values in the masked test image to the defect threshold.

26. The defect detection system of claim 25, wherein the matching metric includes a selected range of pixel values, wherein identifying the one or more comparative regions of the reference image comprises:

identifying one or more regions of the reference image having pixel values within the selected range.

27. The defect detection system of claim 26, wherein the pixel values include grayscale values.

28. The defect detection system of claim 26, wherein the pixel value distribution includes pixel values for pixels within the target region and the one or more comparative regions in the test image.

29. The defect detection system of claim 28, wherein the pixel value distribution includes a histogram of the pixel values for the pixels within the target region and the one or more comparative regions in the test image.

30. The defect detection system of claim 28, wherein defining the defect threshold for the target region based on the pixel distribution comprises:

defining the defect threshold for the target region to identify outliers associated with a tail of the pixel value distribution.

31. The defect detection system of claim 28, wherein determining whether the test image contains a defect by comparing a distribution of pixel values in the masked test image to the defect threshold comprises:

determining whether the target region or any of the one or more comparative regions of the test image contains a defect based on the defect threshold.

32. The defect detection system of claim 25, wherein the target region includes a single target pixel.

33. The defect detection system of claim 32, wherein identifying the one or more comparative regions of the reference image comprises:

defining a vicinity pattern including a defined layout of pixels;

defining a target vicinity in the reference image arranged according to the vicinity pattern, wherein the target vicinity includes the target pixel, wherein the matching metric includes a pixel value distribution of the target vicinity; and

identifying one or more comparative vicinities of the reference image arranged according to the vicinity pattern based on the matching metric, wherein the one or more comparative regions include the one or more comparative vicinities.

34. The defect detection system of claim 33, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference image based on locality-sensitive hatching.

35. The defect detection system of claim 33, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference image based on kernalized locality-sensitive hatching.

36. The defect detection system of claim 33, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference image based on a pattern-matching technique.

37. The defect detection system of claim 33, wherein identifying the one or more comparative vicinities of the reference image comprises:

identifying one or more comparative vicinities of the reference images based on design data.

38. The defect detection system of claim 33, further comprising:

storing one or more locations of the one or more comparative vicinities in a database.

39. The defect detection system of claim 38, wherein the database comprises:

an indexed database.

40. The defect detection system of claim 33, wherein defining the defect threshold for the target region in the test image based on the one or more comparative regions in the test image comprises:

calculating one or more pixel value distributions of the one or more comparative vicinities of the test image;

estimating a pixel value distribution in the target vicinity of the test image based on the pixel value distributions of the one or more comparative vicinities of the test image; and

defining the defect threshold for the target pixel based on the estimated pixel value distribution in the target vicinity.

41. The defect detection system of claim 40, wherein determining whether the target region of the test image contains a defect comprises:

determining whether the target region of the test image contains a defect based on a comparison of the estimated pixel value distribution and an actual pixel value distribution of the target vicinity in the test image.

42. The defect detection system of claim 33, wherein a size of the vicinity pattern is based on an interaction function of a measurement tool used to generate the target image.

43. The defect detection system of claim 33, wherein a size of the vicinity pattern is based on a resolution of a measurement tool used to generate the target image.

44. The defect detection system of claim 25, wherein reference data comprises:

at least one of a reference image or design data.

45. The defect detection system of claim 44, wherein the reference image is based on a golden die.

46. The defect detection system of claim 44, wherein the reference image is based on at least one reference die.

47. The defect detection system of claim 44, wherein the reference image comprises:

a median reference die.

48. The defect detection system of claim 44, wherein the design data comprises:

at least one of netlist data or a design layout of one or more structures.

49. The defect detection system of claim 25, wherein the beam of illumination comprises:

at least one of a beam of photons or a beam of particles.

50. The defect detection system of claim 49, wherein the beam of particles comprises:

at least one of a beam of electrons or ions.

51. The defect detection system of claim 25, wherein the set of illumination optics comprises:

at least one of photon optics or particle optics.

52. The defect detection system of claim 25, wherein the detector comprises:

at least one of a photon detector or a particle detector.

53. A defect detection system comprising:

an inspection sub-system comprising;

an illumination source configured to generate a beam of illumination;

a set of illumination optics to direct the beam of illumination to a sample; and

a detector configured to collect illumination emanating from the sample; and

a controller communicatively coupled to the detector, the controller including a memory device and one or more processors configured to execute program instructions configured to cause the one or more processors to;

acquire a reference image;

select a target pixel of the reference image;

define a vicinity pattern including a defined layout of pixels;

define a target vicinity in the reference image arranged according to the vicinity pattern, wherein the target vicinity includes the target pixel;

identify, based on a matching metric, one or more comparative vicinities of the reference image corresponding to a target region, wherein the matching metric includes a pixel value distribution of the target vicinity;

acquire a test image;

mask the test image with the target region of the reference image and the one or more comparative vicinities of the reference image;

calculate one or more pixel value distributions of the one or more comparative vicinities of the test image;

estimate a pixel value distribution in the target vicinity of the test image based on the pixel value distributions of the one or more comparative vicinities of the test image;

defining a defect threshold for the target pixel based on the estimated pixel value distribution in the target vicinity; and

determine whether the target pixel of the test image contains a defect based on the defect threshold.

54. A defect detection method comprising:

acquiring a reference image;

selecting a target region of the reference image;

identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region;

acquiring a test image;

masking the test image with the target region of the reference image and the one or more comparative regions of the reference to generate a masked test image;

determining a pixel value distribution including pixel values for pixels within the target region and one or more comparative regions in the test image;

defining a defect threshold to identify outliers associated with a tail of the pixel value distribution; and

determining whether the test image contains a defect by comparing a distribution of pixel values in the masked test image to the defect threshold.