Contour based defect detection

• US 10,395,362 B2
• Filed: 02/14/2018
• Issued: 08/27/2019
• Est. Priority Date: 04/07/2017
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation
• Original Assignee: KLA-Tencor Corporation
• Inventors: Gupta, Ajay, Mahadevan, Mohan, Venkataraman, Sankar, Yang, Hedong, Karsenti, Laurent, Carmon, Yair, Bullkich, Noga, Danino, Udy
• Primary Examiner(s): Ton, Tri T
• Application Number:
  US15/896,060
• Publication Number:
  US 20180293721A1
• Time in Patent Office: 559 Days
• Field of Search: 3562371-2376, 3562391-2398
• CPC Class Codes: G01N 2021/8867 using sequentially two or more inspection runs, e.g. coarse and fine, or detecting then analysing
• CPC Class Codes: G01N 2021/8887 based on image processing techniques
• CPC Class Codes: G01N 21/8851 Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002
• CPC Class Codes: G01N 21/9501 Semiconductor wafers manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G01N 21/956 Inspecting patterns on the surface of objects contactless testing of electronic circuits G01R31/308; testing currency G07D; manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G01N 21/95607 using a comparative method
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/08 Learning methods
• 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 7/001 using an image reference approach
• CPC Class Codes: G06T 7/564 from contours
• 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 defects in patterns formed on a specimen are provided. One system includes one or more components executed by one or more computer subsystems, and the component(s) include first and second learning based models. The first learning based model generates simulated contours for the patterns based on a design for the specimen, and the simulated contours are expected contours of a defect free version of the patterns in images of the specimen generated by an imaging subsystem. The second learning based model is configured for generating actual contours for the patterns in at least one acquired image of the patterns formed on the specimen. The computer subsystem(s) are configured for comparing the actual contours to the simulated contours and detecting defects in the patterns formed on the specimen based on results of the comparing.

Claims

1. A system configured to detect defects in patterns formed 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 the images of patterns formed on the specimen; and

one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a first learning based model and a second learning based model, wherein the first and second learning based models are deep learning based models, wherein the first learning based model is configured for generating simulated contours for the patterns based on a design for the specimen input to the first learning based model by the one or more computer subsystems, wherein the simulated contours are expected contours of a defect free version of the patterns in the images of the specimen generated by the imaging subsystem, and wherein the second learning based model is configured for generating actual contours for the patterns in at least one of the acquired images of the patterns formed on the specimen input to the second learning based model by the one or more computer subsystems; and

wherein the one or more computer subsystems are further configured for;

comparing the actual contours to the simulated contours; and

detecting defects in the patterns formed on the specimen based on results of the comparing.

2. The system of claim 1, wherein the results of the comparing comprise quantitative differences between dimensions of a first of the patterns in the design and the first of the patterns in the at least one of the acquired images of the patterns formed on the specimen determined based on differences between the actual and simulated contours, and wherein detecting the defects comprises applying a threshold to the quantitative differences between the dimensions.

3. The system of claim 1, wherein the results of the comparing comprise quantitative differences between the actual and simulated contours for each of the pixels of each of the patterns in the at least one of the acquired images.

4. The system of claim 1, wherein the one or more computer subsystems are further configured for detecting hot spots in the design based on the detected defects.

5. The system of claim 1, wherein the design input to the first learning based model does not include features of the design that will not be printed on the specimen.

6. The system of claim 1, wherein the first and second learning based models are adaptable to different pattern types.

7. The system of claim 1, wherein the first and second learning based models are adaptable to different patterns densities.

8. The system of claim 1, wherein the first and second learning based models are adaptable to patterns in different layer types.

9. The system of claim 1, wherein the first and second learning based models are adaptable to images generated by the imaging subsystem with one or more different imaging parameters.

10. The system of claim 1, wherein the one or more computer subsystems are further configured for training the first learning based model using a training data set comprising different portions of at least one training design for at least one training specimen and corresponding contour information extracted from training images of the at least one training specimen with a ground truth method.

11. The system of claim 1, wherein the one or more computer subsystems are further configured for training the second learning based model using a training data set comprising different training images generated by the imaging subsystem for at least one training specimen on which at least one training design was formed and corresponding contour information extracted from the different training images with a ground truth method.

12. The system of claim 1, wherein the second learning based model has a VGG network architecture.

13. The system of claim 1, wherein the second learning based model is further configured as a holistically-nested edge detection model.

14. The system of claim 1, wherein the first learning based model is further configured as a convolutional neural network.

15. The system of claim 1, wherein the first learning based model is further configured as a deep generative model using variational auto-encoders.

16. The system of claim 1, wherein the one or more computer subsystems are further configured for performing an additional comparing step in which the actual contours for the same patterns formed in different dies on the specimen are compared to each other and detecting defects in the same patterns based on results of the additional comparing step.

17. The system of claim 1, wherein the one or more computer subsystems are further configured for classifying the detected defects.

18. The system of claim 1, wherein the one or more computer subsystems are further configured for classifying the detected defects using an additional learning based model.

19. The system of claim 1, wherein the system is further configured as a metrology tool.

20. The system of claim 1, wherein the system is further configured as an inspection tool.

21. The system of claim 1, wherein the system is further configured as a defect review tool.

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

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

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

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

26. A non-transitory computer-readable medium, storing program instructions executable on one or more computer subsystems for performing a computer-implemented method for detecting defects in patterns formed on a specimen, wherein the computer-implemented method comprises:

acquiring images of patterns formed on a specimen generated by an imaging subsystem with the one or more computer subsystems, 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;

generating simulated contours for the patterns based on a design for the specimen input to a first learning based model by the one or more computer subsystems, wherein the simulated contours are expected contours of a defect free version of the patterns in the images of the specimen generated by the imaging subsystem;

generating actual contours for the patterns in at least one of the acquired images of the patterns formed on the specimen input to a second learning based model by the one or more computer subsystems, wherein one or more components are executed by the one or more computer subsystems, wherein the one or more components comprise the first and second learning based models, and wherein the first and second learning based models are deep learning based models;

comparing the actual contours to the simulated contours; and

detecting defects in the patterns formed on the specimen based on results of the comparing.

27. A computer-implemented method for detecting defects in patterns formed on a specimen, comprising:

acquiring images of patterns formed on a specimen generated by an imaging subsystem with one or more computer subsystems, 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;

generating simulated contours for the patterns based on a design for the specimen input to a first learning based model by the one or more computer subsystems, wherein the simulated contours are expected contours of a defect free version of the patterns in the images of the specimen generated by the imaging subsystem;

generating actual contours for the patterns in at least one of the acquired images of the patterns formed on the specimen input to a second learning based model by the one or more computer subsystems, wherein one or more components are executed by the one or more computer subsystems, wherein the one or more components comprise the first and second learning based models, and wherein the first and second learning based models are deep learning based models;

comparing the actual contours to the simulated contours; and

detecting defects in the patterns formed on the specimen based on results of the comparing.