Automatic defect detection and classification for high throughput electron channeling contrast imaging

• US 9,739,728 B1
• Filed: 06/20/2016
• Issued: 08/22/2017
• Est. Priority Date: 06/20/2016
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bedell, Stephen W., Mo, Renee T., Mukherjee, Kunal, Ott, John A., Sadana, Devendra K., Wacaser, Brent A.
• Primary Examiner(s): Nguyen, Kiet T
• Application Number:
  US15/187,467
• Time in Patent Office: 428 Days
• Field of Search: 250307, 250306, 250310
• CPC Class Codes: G01N 2223/401 image processing
• CPC Class Codes: G01N 2223/6116 semiconductor wafer
• CPC Class Codes: G01N 2223/646 flaws, defects
• CPC Class Codes: G01N 23/203 Measuring back scattering
• CPC Class Codes: G06T 7/0004 Industrial image inspection

Abstract

Imaging and processing techniques are employed to identify crystalline defects obtained by ECCI from surrounding topography and is combined with defect counting and automatic classification.

Claims

1. A method for automatic detection and classification of crystalline defects of a crystalline material, said method comprising:

obtaining a first image of a crystalline material utilizing electron channeling contrast imaging (ECCI) under a particular channeling condition;

altering the ECCI conditions to obtain a second image;

distinguishing crystalline defects from other features present in said second image;

tallying the number of said crystalline defects;

processing, using an algorithm, features of said crystalline defects in said second image to determine a defect fingerprint for each of said crystalline defects; and

classifying the defect type of each of said crystalline defects by comparing said defect fingerprint to a library containing images of defect fingerprints of known defect types.

2. The method of claim 1, wherein said altering said ECCI conditions comprises tilting said crystalline material.

3. The method of claim 1, wherein said altering said ECCI conditions comprises tilting said crystalline material to a complementary channeling condition.

4. The method of claim 1, wherein said altering said ECCI conditions comprises changing the accelerating voltage of an electron beam, modifying the optimum channeling conditions.

5. The method of claim 1, wherein said altering said ECCI conditions comprises changing a detector configuration to collect different finds of complementary signal.

6. The method of claim 1, wherein said processing comprises generating a line profile and two-dimensional cropped images of said crystalline defect.

7. The method of claim 1, further comprising:

tallying and storing the number of each type of crystalline defect and the total area scanned; and

determining the number of each type of crystalline defects per unit area of said crystalline material.

8. The method of claim 7, wherein said tallying comprises counting a number of frames with a specific type of said crystalline defect and without said specific type of crystalline defect.

9. The method of claim 1, wherein said crystalline material comprises a plurality of patterned structures present on a surface of a substrate, each patterned structure of said plurality of patterned structures is misaligned relative to the known crystallographic angles at least one type of crystalline defect present in each patterned structure.

10. A method for automatic detection of crystalline defects of a crystalline material, said method comprising:

providing a substrate containing a plurality of patterned structures comprising a crystalline material, wherein each of said patterned structure is misaligned relative to at least one type of crystalline defect present in each patterned structure; and

obtaining an image of each patterned structure utilizing electron channeling contrast imaging (ECCI).

11. The method of claim 10, wherein each patterned structure is a crystalline fin.

12. The method of claim 10, further comprising:

altering the ECCI conditions to provide second images;

distinguish crystalline defects from other features present in said second images;

tallying the number of said crystalline defects;

processing, using an algorithm, features of said crystalline defects in said second images to determine the defect fingerprint for each of said crystalline defects; and

classifying the defect type of each of said crystalline defects by comparing said defect fingerprint to a library containing images of defect fingerprints of known defect types.

13. The method of claim 12, wherein said altering said ECCI conditions comprises tilting said crystalline material.

14. The method of claim 12, wherein said altering said ECCI conditions comprises tilting said crystalline material to a complementary channeling condition.

15. The method of claim 12, wherein said altering said ECCI conditions comprises changing the accelerating voltage of the electron beam, modifying the optimum channeling conditions.

16. The method of claim 12, wherein said altering said ECCI conditions comprises changing the detector configuration to collect different finds of complementary signal.

17. The method of claim 12, wherein said processing comprises generating a line profile and two-dimensional cropped images of said crystalline defect.

18. The method of claim 12, further comprising:

tallying and storing the number of each type of crystalline defect and the total area scanned; and

determining the number of each type of crystalline defects per unit area of said crystalline material.

19. The method of claim 18, wherein said tallying comprises counting a number of frames with a specific type of said crystalline defect and without said specific type of said crystalline defect.