Outlier detection on pattern of interest image populations

• US 9,767,548 B2
• Filed: 04/21/2016
• Issued: 09/19/2017
• Est. Priority Date: 04/24/2015
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation
• Original Assignee: KLA-Tencor Corporation
• Inventors: Banerjee, Saibal, Kulkarni, Ashok V.
• Primary Examiner(s): Niu, Feng
• Application Number:
  US15/135,465
• Publication Number:
  US 20160314578A1
• Time in Patent Office: 516 Days
• Field of Search: None
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

Methods and systems for identifying outliers in multiple instances of a pattern of interest (POI) are provided. One system includes one or more computer subsystems configured for acquiring images generated by an imaging subsystem at multiple instances of a POI within a die formed on the specimen. The multiple instances include two or more instances that are located at aperiodic locations within the die. The computer subsystem(s) are also configured for determining a feature of each of the images generated at the multiple instances of the POI. In addition, the computer subsystem(s) are configured for identifying one or more outliers in the multiple instances of the POI based on the determined features.

Claims

1. A system configured to identify outliers in multiple instances of a pattern of interest on a specimen, comprising:

an imaging subsystem comprising at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to a specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy; and

one or more computer subsystems configured for;

acquiring images generated by the imaging subsystem at multiple instances of a pattern of interest within a die formed on the specimen, wherein the multiple instances comprise two or more instances that are located at aperiodic locations within the die;

determining a feature of each of the images generated at the multiple instances of the pattern of interest; and

identifying one or more outliers in the multiple instances of the pattern of interest based on the determined features.

2. The system of claim 1, wherein the one or more computer subsystems are further configured for separately performing said acquiring, said determining, and said identifying for a different pattern of interest within the die formed on the specimen.

3. The system of claim 2, wherein the multiple instances of the pattern of interest and the different pattern of interest cover a majority of the die.

4. The system of claim 1, wherein said determining and said identifying do not comprise generating a difference image from two or more of the images generated at the multiple instances of the pattern of interest.

5. The system of claim 1, wherein the one or more computer subsystems are further configured for receiving information for the pattern of interest and identifying all of the multiple instances of the pattern of interest in the die based on the information.

6. The system of claim 1, wherein the one or more computer subsystems are further configured for performing said acquiring, said determining, and said identifying for a combination of the multiple instances of the pattern of interest within the die and at least one additional instance of the pattern of interest within at least one additional die on the specimen.

7. The system of claim 1, wherein the one or more computer subsystems are further configured for performing said acquiring, said determining, and said identifying for a combination of the multiple instances of the pattern of interest within the die and at least one additional instance of the pattern of interest on at least one additional specimen.

8. The system of claim 1, wherein said identifying comprises identifying and discarding one or more of the images based on one or more extreme values of the feature determined for each of the images, determining a central tendency measure for remaining images, scoring all of the images based on deviation of their feature values from the central tendency measure, and identifying the one or more outliers based on results of said scoring.

9. The system of claim 1, wherein said identifying comprises determining rejection or trimming scores for all of the images based on one or more values for the feature determined for each of the images and identifying the one or more outliers by applying a threshold to the rejection or trimming scores.

10. The system of claim 1, wherein the one or more computer subsystems are further configured for, prior to said determining, reducing image artifacts in the acquired images caused by the imaging subsystem or the specimen.

11. The system of claim 1, wherein the images generated by the imaging subsystem are patch images, wherein the one or more computer subsystems are further configured for, prior to said determining, identifying a sub-image in one of the patch images generated at one of the multiple instances of the pattern of interest, aligning the sub-image to each of the patch images generated at the multiple instances of the pattern of interest, and identifying a sub-image in each of the patch images generated at the multiple instances of the pattern of interest that corresponds to the pattern of interest based on results of the aligning, and wherein the feature of each of the images is determined based on only the sub-images in each of the images.

12. The system of claim 1, wherein the one or more computer subsystems are further configured for identifying the multiple instances of the pattern of interest that have different patterns surrounding the pattern of interest based on the determined features or another feature determined for each of the images.

13. The system of claim 1, wherein the one or more computer subsystems are further configured for identifying one or more regions in the pattern of interest having more noise than other regions in the pattern of interest based on results of said identifying and repeating said identifying based on the determined features and weights for the identified one or more regions.

14. The system of claim 1, wherein the one or more computer subsystems are further configured for identifying one or more regions in the pattern of interest having more noise than other regions in the pattern of interest based on results of said identifying and selecting the identified one or more regions as measurement sites for a metrology process performed on the specimen.

15. The system of claim 14, wherein the one or more computer subsystems are further configured for determining one or more parameters of measurements performed at the measurement sites during the metrology process based on the identified one or more regions.

16. The system of claim 1, wherein the one or more computer subsystems are further configured for:

determining a function that describes a correlation between the feature or another feature of the images generated at the multiple instances of the pattern of interest and results of a metrology process performed on the specimen; and

identifying one or more additional outliers in the multiple instances of the pattern of interest based on the feature or the other feature and the function.

17. The system of claim 1, wherein said determining comprises generating difference images for each of the images by subtracting a reference image generated from a different die formed on the specimen from each of the images and determining the feature of each of the images from its corresponding difference image.

18. The system of claim 1, wherein the one or more computer subsystems are further configured for performing said acquiring, said determining, and said identifying for one or more additional dies formed on the specimen and comparing results of the identifying for the die and the one or more additional dies to identify which of the die and the one or more additional dies are outliers.

19. The system of claim 1, wherein the multiple instances of the pattern of interest are located within only one of multiple reticle fields within the die formed on the specimen.

20. The system of claim 1, wherein the die is a single instance of multiple dies formed on the specimen, and wherein the multiple instances for which said acquiring, said determining, and said identifying are performed are located only within the single instance of the multiple dies.

21. The system of claim 1, wherein the die is the only die formed on the specimen.

22. The system of claim 1, wherein at least a portion of the die and at least a portion of an additional die formed on the specimen are located in a region on the specimen, and wherein the multiple instances of the pattern of interest for which said acquiring, said determining, and said identifying are performed are located in at least the portion of the die and at least the portion of the additional die.

23. The system of claim 1, wherein the feature consists of a single value.

24. The system of claim 1, wherein the feature comprises an ordered multiplicity of values.

25. The system of claim 1, wherein the specimen comprises a wafer.

26. The system of claim 1, wherein the specimen comprises a reticle.

27. The system of claim 1, wherein the energy directed to the specimen comprises light, and wherein the energy detected from the specimen comprises light.

28. The system of claim 1, wherein the energy directed to the specimen comprises electrons, and wherein the energy detected from the specimen comprises electrons.

29. A non-transitory computer-readable medium, storing program instructions executable on a computer system for performing a computer-implemented method for identifying outliers in multiple instances of a pattern of interest on a specimen, wherein the computer-implemented method comprises:

acquiring images generated by an imaging subsystem at multiple instances of a pattern of interest within a die formed on the specimen, wherein the multiple instances comprise two or more instances that are located at aperiodic locations within the die, wherein the imaging subsystem comprises at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to the specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy;

determining a feature of each of the images generated at the multiple instances of the pattern of interest; and

identifying one or more outliers in the multiple instances of the pattern of interest based on the determined features, wherein said acquiring, said determining, and said identifying are performed by one or more computer subsystems.

30. A computer-implemented method for identifying outliers in multiple instances of a pattern of interest, on a specimen, comprising:

acquiring images generated by an imaging subsystem at multiple instances of a pattern of interest within a die formed on the specimen, wherein the multiple instances comprise two or more instances that are located at aperiodic locations within the die, wherein the imaging subsystem comprises at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to the specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy;

determining a feature of each of the images generated at the multiple instances of the pattern of interest; and

identifying one or more outliers in the multiple instances of the pattern of interest based on the determined features, wherein said acquiring, said determining, and said identifying are performed by one or more computer subsystems.