Computer-implemented methods for detecting and/or sorting defects in a design pattern of a reticle

• US 7,729,529 B2
• Filed: 12/07/2004
• Issued: 06/01/2010
• Est. Priority Date: 12/07/2004
• Status: Active Grant

• Current Assignee: Kla-Tencor Technologies Corporation (KLA Corporation)
• Original Assignee: Kla-Tencor Technologies Corporation (KLA Corporation)
• Inventors: Randall, David, Wu, Kenong, Ynzunza, Ramon, Tribble, Ariel, Nafisi, Kourosh, Kulkarni, Ashok, Gao, Lisheng, Kowalski, Michal, Peterson, Ingrid B.
• Primary Examiner(s): Bali; Vikkram
• Assistant Examiner(s): ENTEZARI, MICHELLE M
• Application Number:
  US11/005,658
• Publication Number:
  US 20060291714A1
• Time in Patent Office: 2,002 Days
• Field of Search: 382/149
• US Class Current: 382/149
• CPC Class Codes: G01N 2021/95676 Masks, reticles, shadow masks
• CPC Class Codes: G01N 21/95607 using a comparative method
• CPC Class Codes: G03F 1/84 Inspecting
• CPC Class Codes: G03F 7/7065 Defects, e.g. optical inspection of patterned layer for defects
• CPC Class Codes: G03F 7/70666 Aerial image, i.e. measuring the image of the patterned exposure light at the image plane of the projection system

Abstract

Various computer-implemented methods are provided. One method for sorting defects in a design pattern of a reticle includes searching for defects of interest in inspection data using priority information associated with individual defects in combination with one or more characteristics of a region proximate the individual defects. The priority information corresponds to modulation levels associated with the individual defects. The inspection data is generated by comparing images of the reticle generated for different values of a lithographic variable. The images include at least one reference image and at least one modulated image. A composite reference image can be generated from two or more reference images. The method also includes assigning one or more identifiers to the defects of interest. The identifier(s) may include, for example, a defect classification and/or an indicator identifying if the defects of interest are to be used for further processing.

Claims

1. A computer-implemented method for sorting defects in a design pattern of a reticle, comprising:

searching for defects of interest in inspection data using priority information and defect attributes associated with individual defects in combination with one or more characteristics of a region proximate the individual defects and one or more characteristics of the individual defects, wherein the inspection data is generated by comparing images of the reticle to each other to detect the individual defects in the design pattern of the reticle, wherein the images that are compared to each other are generated for different values of a lithographic variable, wherein the images comprise at least one reference image and at least one modulated image, and wherein the priority information is derived from a relationship between the individual defects and their corresponding modulation levels of the lithographic variable; and

assigning one or more identifiers to the defects of interest.

2. The method of claim 1, wherein the defect attributes comprise inspection parameter information, simple defect information comprising location, size and intensity magnitude, and a relationship among individual defects.

3. The method of claim 1, further comprising filtering the individual defects based on the priority information and the defect attributes.

4. The method of claim 3, wherein criteria for the filtering can be selected by a user.

5. The method of claim 1, wherein the one or more characteristics of the region proximate the individual defects and on the individual defects are derived from the at least one reference image and the at least one modulated image, respectively.

6. The method of claim 1, wherein the one or more characteristics of the region proximate the individual defects are selected by a user.

7. The method of claim 1, further comprising grouping the defects of interest based on the one or more characteristics of the region proximate the individual defects.

8. The method of claim 7, further comprising analyzing one or more characteristics of the region proximate one or more of the individual defects in a group to determine if the group is an irrelevant defect group.

9. The method of claim 1, further comprising grouping the defects of interest based on the one or more characteristics of the region proximate the individual defects in combination with the one or more characteristics of the defects of interest.

10. The method of claim 9, further comprising refining the grouping of the defects of interest based on similarity of the individual defects to a selected defect based on the one or more characteristics of the region proximate the individual defects or the individual defects themselves.

11. The method of claim 1, wherein the one or more identifiers comprise a defect classification.

12. The method of claim 1, wherein the one or more identifiers comprise an indicator identifying if the defects of interest are to be selected for future processing.

13. The method of claim 1, wherein the one or more identifiers comprise an indicator identifying if the defects of interest are to be selected for future processing, and wherein said assigning the one or more identifiers is performed through a sampling algorithm performed automatically based on the priority information and the one or more identifiers.

14. The method of claim 1, further comprising comparing the defects of interest to inspection data generated by design rule checking performed on design pattern data of the reticle to determine if the defects of interest correlate to design rule checking defects.

15. The method of claim 14, further comprising removing from the defects of interest the defects that do not correlate with results of the design rule checking.

16. The method of claim 1, wherein the one or more characteristics of the region comprise one or more characteristics of the region extracted from a GDS or aerial image.

17. The method of claim 1, wherein the one or more characteristics of the region comprise one or more characteristics of the region determined from a high resolution image.

18. A storage medium, comprising program instructions executable on a computer system to perform a computer-implemented method for sorting defects in a design pattern of a reticle, wherein the computer-implemented method comprises:

searching for defects of interest in inspection data using priority information and defect attributes associated with individual defects in combination with one or more characteristics of a region proximate the individual defects and one or more characteristics of the individual defects, wherein the inspection data is generated by comparing images of the reticle to each other to detect the individual defects in the design pattern of the reticle, wherein the images that are compared to each other are generated for different values of a lithographic variable, wherein the images comprise at least one reference image and at least one modulated image, and wherein the priority information is derived from a relationship between the individual defects and their corresponding modulation levels of the lithographic variable; and

assigning one or more identifiers to the defects of interest.

19. A system configured to sort defects in a design pattern of a reticle, comprising:

an inspection tool configured to generate image data for a wafer that contains information about the design pattern printed on the wafer by the reticle;

a processor configured to receive the image data; and

a storage medium comprising program instructions executable on the processor for performing a computer-implemented method, wherein the computer-implemented method comprises;

generating inspection data by comparing the image data to each other to detect individual defects in the design pattern of the reticle, wherein the image data that is compared to each other is generated for different values of a lithographic variable, and wherein the image data comprises at least one reference image and at least one modulated image;

searching for defects of interest in the inspection data using priority information and defect attributes associated with the individual defects in combination with one or more characteristics of a region proximate the individual defects and one or more characteristics of the individual defects, wherein the priority information is derived from a relationship between the individual defects and their corresponding modulation levels of the lithographic variable; and

assigning one or more identifiers to the defects of interest.