Image based specimen process control

• US 10,181,185 B2
• Filed: 01/09/2017
• Issued: 01/15/2019
• Est. Priority Date: 01/11/2016
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation
• Original Assignee: KLA-Tencor Corporation
• Inventors: Park, Allen, Gao, Lisheng, Kulkarni, Ashok, Banerjee, Saibal, Gu, Ping, Rong, Songnian, Bhaskar, Kris
• Primary Examiner(s): Ky, Kevin
• Application Number:
  US15/402,197
• Publication Number:
  US 20170200264A1
• Time in Patent Office: 736 Days
• Field of Search: None
• CPC Class Codes: G01N 21/9501 Semiconductor wafers manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/10061 from scanning electron microscope
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/001 using an image reference approach
• CPC Class Codes: H01L 22/12 for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions electrical measurement of diffusions H01L22/14
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

Methods and systems for detecting anomalies in images of a specimen are provided. One system includes one or more computer subsystems configured for acquiring images generated of a specimen by an imaging subsystem. The computer subsystem(s) are also configured for determining one or more characteristics of the acquired images. In addition, the computer subsystem(s) are configured for identifying anomalies in the images based on the one or more determined characteristics without applying a defect detection algorithm to the images or the one or more characteristics of the images.

Claims

1. A system configured to detect anomalies in images of a specimen, comprising:

an imaging subsystem configured for generating images of a specimen by directing energy to and detecting energy from the specimen, wherein the imaging subsystem comprises at least one energy source configured for generating the energy directed to the specimen and at least one detector configured for detecting the energy from the specimen; and

one or more computer subsystems coupled to the imaging subsystem, wherein the one or more computer subsystems are configured for;

acquiring the images generated of the specimen, wherein the acquired images comprise optical images or electron beam images;

determining one or more characteristics of the acquired images; and

identifying anomalies in the optical images or the electron beam images based on the one or more determined characteristics without applying a predetermined defect detection algorithm to the images or the one or more characteristics of the images, and

wherein the one or more computer subsystems comprise one or more components executed by the one or more computer subsystems, and wherein the one or more components comprise a learning based model configured for performing said determining the one or more characteristics of the acquired images and for identifying the one or more characteristics that are determined for the acquired images.

2. The system of claim 1, wherein the acquired images for which the one or more characteristics are determined are not processed by the one or more computer subsystems prior to said determining.

3. The system of claim 1, wherein the one or more computer subsystems are further configured for identifying one or more problems with a device being formed on the specimen based on the identified anomalies.

4. The system of claim 1, wherein the one or more computer subsystems are further configured for identifying one or more problems with a process performed on the specimen based on the identified anomalies.

5. The system of claim 1, wherein the one or more computer subsystems are further configured for altering a process performed on the specimen based on the anomalies to thereby improve yield of the process.

6. The system of claim 1, wherein said determining is performed in real time while the imaging subsystem is generating the images of the specimen.

7. The system of claim 1, wherein said determining is performed after the imaging subsystem is finished generating the images of the specimen.

8. The system of claim 1, wherein said identifying is performed based on the one or more determined characteristics in combination with one or more parameters of the imaging subsystem used for generating the images.

9. The system of claim 1, wherein the one or more characteristics of the acquired images comprise one or more local characteristics of the acquired images.

10. The system of claim 1, wherein the one or more characteristics of the acquired images comprise one or more global characteristics of the acquired images.

11. The system of claim 1, wherein the one or more computer subsystems are further configured for stacking two or more of the acquired images, detecting defects in the stacked two or more of the acquired images, and determining one or more attributes of the detected defects based on the stacked two or more of the acquired images.

12. The system of claim 1, wherein the one or more characteristics comprise at least one characteristic of the acquired images linked to parameters of the imaging subsystem used for acquiring the images.

13. The system of claim 1, wherein the one or more computer subsystems are further configured for training the learning based model with the acquired images and one or more additional types of information for the specimen, and wherein the learning based model is further configured for predicting information for defects on the specimen based on the acquired images and the one or more additional types of the information.

14. The system of claim 13, wherein the one or more additional types of information for the specimen comprise design data for the specimen.

15. The system of claim 13, wherein the one or more additional types of information for the specimen comprise electrical test and measurement, data for the specimen.

16. The system of claim 13, wherein the one or more additional types of information for the specimen comprise process tool data for the specimen.

17. The system of claim 13, wherein the training comprises supervised training.

18. The system of claim 13, wherein the training comprises unsupervised training.

19. The system of claim 13, wherein the learning based model comprises a deep learning based model.

20. The system of claim 13, wherein the learning based model comprises a convolutional neural network.

21. The system of claim 13, wherein the learning based model comprises an autoencoder.

22. The system of claim 1, wherein the one or more computer subsystems are further configured for predicting defect information for the specimen based on the one or more determined characteristics and one or more additional types of information for the specimen.

23. The system of claim 1, wherein the one or more computer subsystems are further configured for predicting yield criticality of defects on the specimen based on the one or more determined characteristics and one or more additional types of information for the specimen.

24. The system of claim 1, wherein the one or more computer subsystems are further configured for predicting performance of devices being formed on the specimen based on the one or more determined characteristics and one or more additional types of information for the specimen.

25. The system of claim 1, wherein the one or more computer subsystems are further configured for creating the one or more characteristics in real time based on one or more additional types of information.

26. The system of claim 1, wherein the one or more computer subsystems are further configured for performing said acquiring, determining, and identifying for multiple specimens, monitoring the one or more characteristics or the anomalies for changes, and altering a parameter of a process tool in response to the changes detected by the monitoring.

27. The system of claim 1, wherein the one or more computer subsystems are further configured for determining one or more spatial characteristics of the anomalies and identifying a potential source of the anomalies based on the determined one or more spatial characteristics.

28. The system of claim 1, wherein the one or more computer subsystems are further configured for stacking two or more of the acquired images and identifying one or more weak points in a design for the specimen based on the stacked two or more of the acquired images.

29. The system of claim 1, wherein the one or more computer subsystems are further configured for creating an index based on the one or more characteristics of the acquired images representing potential impact of one or more attributes of the specimen, corresponding to the one or more characteristics of the images, on performance of devices being formed on the specimen.

30. The system of claim 1, wherein the imaging subsystem is further configured as an optical based imaging subsystem.

31. The system of claim 1, wherein the imaging subsystem is further configured as an electron beam based imaging subsystem.

32. The system of claim 1, wherein the specimen is a wafer.

33. The system of claim 1, wherein the specimen is a reticle.

34. A non-transitory computer-readable medium, storing program instructions executable on one or more computer systems for performing a computer-implemented method for detecting anomalies in images of a specimen, wherein the computer-implemented method comprises:

generating images of a specimen by directing energy to and detecting energy from the specimen with an imaging subsystem, wherein the imaging subsystem comprises at least one energy source configured for generating the energy directed to the specimen and at least one detector configured for detecting the energy from the specimen;

acquiring the images generated of the specimen, wherein the acquired images comprise optical images or electron beam images;

determining one or more characteristics of the acquired images; and

identifying anomalies in the optical images or the electron beam images based on the one or more determined characteristics without applying a predetermined defect detection algorithm to the images or the one or more characteristics of the images, wherein said acquiring, said determining, and said identifying are performed by one or more computer subsystems coupled to the imaging subsystem, wherein the one or more computer subsystems comprise one or more components executed by the one or more computer subsystems, and wherein the one or more components comprise a learning based model configured for performing said determining the one or more characteristics of the acquired images and for identifying the one or more characteristics that are determined for the acquired images.

35. A computer-implemented method for detecting anomalies in images of a specimen, comprising

generating images of a specimen by directing energy to and detecting energy from the specimen with an imaging subsystem, wherein the imaging subsystem comprises at least one energy source configured for generating the energy directed to the specimen and at least one detector configured for detecting the energy from the specimen;

acquiring the images generated of the specimen, wherein the acquired images comprise optical images or electron beam images;

determining one or more characteristics of the acquired images; and

identifying anomalies in the optical images or the electron beam images based on the one or more determined characteristics without applying a predetermined defect detection algorithm to the images or the one or more characteristics of the images, wherein said acquiring, said determining, and said identifying are performed by one or more computer subsystems coupled to the imaging subsystem, wherein the one or more computer subsystems comprise one or more components executed by the one or more computer subsystems, and wherein the one or more components comprise a learning based model configured for performing said determining the one or more characteristics of the acquired images and for identifying the one or more characteristics that are determined for the acquired images.