SYSTEM AND METHOD FOR IDENTIFYING SYSTEMATIC DEFECTS IN WAFER INSPECTION USING HIERARCHICAL GROUPING AND FILTERING

• US 20140355867A1
• Filed: 05/30/2013
• Published: 12/04/2014
• Est. Priority Date: 05/30/2013
• Status: Active Grant

• Current Assignee: Applied Materials Incorporated
• Original Assignee: DMO Systems Limited (Applied Materials Incorporated)
• Inventors: Lin, Jason Zse-Cherng, Juang, Shauh-Teh
• Granted Patent: US 9,142,014 B2
• Time in Patent Office: Days
• US Class Current: 382/149
• CPC Class Codes: G06T 2207/10061 from scanning electron microscope
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

A number of wafers of a same semiconductor device are inspected to generate a plurality of candidate defect lists for identifying systematic defects. Each candidate defect list comprises a plurality of candidate defects obtained from inspecting one of the wafers. Each candidate defect is represented by a plurality of defect attributes including a defect location. The candidate defects in every one or more candidate defect lists are processed as a set by stage one grouping and filtering to generate a stage one defect list for each set. The candidate defects in all the stage one defect lists are then processed together by stage two grouping and filtering to generate a final defect lists for systematic defects. The defect attributes of each defect and a design pattern clip extracted from a design database based on the defect location are used in the hierarchical grouping and filtering.

Claims

1. A method of identifying systematic defects in wafer inspection using hierarchical grouping and filtering, comprising the steps of:

collecting a plurality of candidate defect lists obtained from inspecting a plurality of wafers of a same semiconductor device, each of said candidate defect lists comprising a plurality of candidate defects obtained from inspecting one of said plurality of wafers, each of said candidate defects being represented by a plurality of defect attributes including a defect location;

dividing said plurality of candidate defect lists into a plurality of sets;

performing first stage grouping and filtering for the candidate defects in the candidate defect lists of each set to generate a first stage defect list respectively for each set; and

performing second stage grouping and filtering for the candidate defects in all the first stage defect lists together to generate a final defect list for said systematic defects;

wherein the defect attributes of each candidate defect are used in said first stage grouping and filtering and said second stage grouping and filtering.

2. The method as claimed in claim 1, wherein each of said plurality of sets comprises only one candidate defect list.

3. The method as claimed in claim 1, wherein each of said plurality of sets comprises at least two candidate defect lists.

4. The method as claimed in claim 1, wherein said second stage grouping and filtering uses a recipe different from the recipe used in said first stage grouping and filtering.

5. The method as claimed in claim 1, further comprising the steps of:

forming a group of previous first stage defect lists from all the first stage defect lists used in generating a previous final defect list by excluding at least one of the first stage defect lists;

collecting at least one new set of new candidate defect lists;

performing first stage grouping and filtering for the candidate defects in each new set to generate a new first stage defect list respectively for each new set; and

performing second stage grouping and filtering for the candidate defects in all the new first stage defect lists and the group of previous first stage defect lists together to generate a new final defect list for said systematic defects.

6. The method as claimed in claim 1, wherein said final defect list comprises a plurality of defect groups and each defect group is considered as a systematic defect if a pre-set criteria is satisfied.

7. The method as claimed in claim 6, wherein said pre-set criteria is the number of candidate defects in the defect group exceeding a pre-set threshold.

8. The method as claimed in claim 6, wherein the plurality of defect groups in said final defect list are ranked based on the number of candidate defects in each defect group for determining criticality of the systematic defects.

9. The method as claimed in claim 6, wherein a critical area is defined for an identified systematic defect according to the defect locations of the candidate defects in the defect group corresponding to the identified systematic defect and the critical area is used as a micro care area in future wafer inspection of the same semiconductor device.

10. The method as claimed in claim 6, wherein a design pattern clip extracted from a design database based on the defect location of each candidate defect is used in said first stage grouping and filtering and said second stage grouping and filtering.

11. The method as claimed in claim 10, wherein in said first stage grouping and filtering and said second stage grouping and filtering, candidate defects having an identical design pattern are grouped in a same defect group.

12. The method as claimed in claim 11, wherein the design pattern of the defect group corresponding to an identified systematic defect is used to search the design database for identifying a plurality of critical areas having the same design patterns, and the plurality of critical areas are used as micro care areas in future wafer inspection of the same semiconductor device.

13. The method as claimed in claim 6, wherein a high-resolution electron-beam image pattern is acquired based on the defect location of each candidate defect and used in said first stage grouping and filtering and said second stage grouping and filtering.

14. A distributed computing system for identifying systematic defects in wafer inspection using hierarchical grouping and filtering, comprising:

a data storage system for receiving and storing a plurality of candidate defect lists obtained from inspecting a plurality of wafers of a same semiconductor device, each of said candidate defect lists comprising a plurality of candidate defects obtained from inspecting one of said plurality of wafers, each of said candidate defects being represented by a plurality of defect attributes including a defect location; and

a plurality of computing nodes having executable codes for performing hierarchical grouping and filtering including;

dividing said plurality of candidate defect lists into a plurality of sets;

performing first stage grouping and filtering for the candidate defects in the candidate defect lists of each set to generate a first stage defect list respectively for each set; and

performing second stage grouping and filtering for the candidate defects in all the first stage defect lists together to generate a final defect list for said systematic defects;

wherein the defect attributes of each candidate defect are used in said first stage grouping and filtering and said second stage grouping and filtering.

15. The distributed computing system as claimed in claim 14, wherein each of said plurality of sets comprises only one candidate defect list.

16. The distributed computing system as claimed in claim 14, wherein each of said plurality of sets comprises at least two candidate defect lists.

17. The distributed computing system as claimed in claim 14, wherein said second stage grouping and filtering uses a recipe different from the recipe used in said first stage grouping and filtering.

18. The distributed computing system as claimed in claim 14, wherein said final defect list comprises a plurality of defect groups and each defect group is considered as a systematic defect if a pre-set criteria is satisfied.

19. The distributed computing system as claimed in claim 18, wherein said pre-set criteria is the number of candidate defects in the defect group exceeding a pre-set threshold.

20. The distributed computing system as claimed in claim 18, wherein the plurality of defect groups in said final defect list are ranked based on the number of candidate defects in each defect group for determining criticality of the systematic defects.

21. The distributed computing system as claimed in claim 18, wherein a critical area is defined for an identified systematic defect according to the defect locations of the candidate defects in the defect group corresponding to the identified systematic defect and the critical area is used as a micro care area in future wafer inspection of the same semiconductor device.

22. The distributed computing system as claimed in claim 18, wherein said distributed computing system further comprises a design database, and a design pattern clip extracted from the design database based on the defect location of each candidate defect is used in said first stage grouping and filtering and said second stage grouping and filtering.

23. The distributed computing system as claimed in claim 22, wherein in said first stage grouping and filtering and said second stage grouping and filtering, candidate defects having an identical design pattern are grouped in a same defect group.

24. The distributed computing system as claimed in claim 23, wherein the design pattern of the defect group corresponding to an identified systematic defect is used to search the design database for identifying a plurality of critical areas having the same design patterns, and the plurality of critical areas are used as micro care areas in future wafer inspection of the same semiconductor device.