Generating simulated output for a specimen

Patent metadata

Field	Value
Publication number	US-10043261-B2
Application number	US-201715402094-A
Country	US
Kind code	B2
Filing date	Jan 9, 2017
Priority date	Jan 11, 2016
Publication date	Aug 7, 2018
Grant date	Aug 7, 2018

Abstract

Methods and systems for generating simulated output for a specimen are provided. One method includes acquiring information for a specimen with one or more computer systems. The information includes at least one of an actual optical image of the specimen, an actual electron beam image of the specimen, and design data for the specimen. The method also includes inputting the information for the specimen into a learning based model. The learning based model is included in one or more components executed by the one or more computer systems. The learning based model is configured for mapping a triangular relationship between optical images, electron beam images, and design data, and the learning based model applies the triangular relationship to the input to thereby generate simulated images for the specimen.

First claim

What is claimed is:

1. A system configured to generate simulated output for a specimen, comprising:
one or more computer subsystems configured for acquiring information for a specimen, wherein the information comprises at least one of an actual optical image of the specimen, an actual electron beam image of the specimen, and design data for the specimen; and
one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a learning based model, wherein the learning based model is configured for mapping a triangular relationship between optical images, electron beam images, and design data, wherein the one or more computer subsystems are configured to input the information for the specimen into the learning based model, and wherein the learning based model applies the triangular relationship to the input to thereby generate simulated output for the specimen.

2. The system of claim 1 , wherein the information that is input to the learning based model comprises the actual optical image of the specimen, and wherein the simulated output comprises a simulated electron beam image representing an actual electron beam image generated for the specimen by an electron beam tool.

3. The system of claim 1 , wherein the information that is input to the learning based model comprises the actual optical image of the specimen, and wherein the simulated output comprises simulated design data for the specimen.

4. The system of claim 1 , wherein the information that is input to the learning based model comprises the actual electron beam image of the specimen, and wherein the simulated output comprises simulated design data for the specimen.

5. The system of claim 1 , wherein the information for the specimen further comprises actual optical images of the specimen generated at different values of a parameter of an optical tool, wherein the information that is input to the learning based model comprises the actual optical images of the specimen, and wherein the simulated output comprises a simulated electron beam image of the specimen representing an actual electron beam image generated for the specimen by an electron beam tool.

6. The system of claim 1 , wherein the information for the specimen further comprises actual optical images of the specimen generated at different values of a parameter of an optical tool, wherein the information that is input to the learning based model comprises the actual optical images of the specimen, wherein the input to the learning based model further comprises another different value of the parameter of the optical tool, and wherein the simulated output comprises a simulated optical image of the specimen representing an optical image generated at the other different value of the parameter of the optical tool.

7. The system of claim 1 , wherein the information that is input to the learning based model comprises the actual optical image of the specimen and the design data for the specimen, and wherein the simulated output comprises a simulated electron beam image for the specimen representing an actual electron beam image generated for the specimen by an electron beam tool.

8. The system of claim 1 , wherein the information for the specimen further comprises actual optical images of the specimen corresponding to different values of a parameter of a process performed on the specimen, wherein the information that is input to the learning based model comprises the actual optical images of the specimen, wherein the input to the learning based model further comprises another different value of the parameter of the process, and wherein the simulated output comprises a simulated optical image of the specimen corresponding to the other different value of the parameter of the process.

9. The system of claim 1 , wherein the information for the specimen further comprises actual optical images of the specimen generated at different values of a parameter of an optical tool, wherein the information that is input to the learning based model comprises the actual optical images of the specimen and the design data for the specimen, and wherein the simulated output comprises defect classifications for defects detected on the specimen.

10. The system of claim 1 , wherein the information that is input to the learning based model comprises run time input, and wherein the information for the run time input comprises information for a pattern not included in input to the learning based model used for training the learning based model.

11. The system of claim 1 , wherein the learning based model is not configured for generating the simulated output for the specimen by performing pixel-value interpolation.

12. The system of claim 1 , wherein the learning based model is not configured for generating the simulated output for the specimen by performing pixel-value extrapolation.

13. The system of claim 1 , wherein the learning based model is further configured for mapping a relationship between patterned features on the specimen and one or more of the optical images, the electron beam images, and the design data.

14. The system of claim 1 , wherein the information for the specimen further comprises two or more actual optical images of the specimen, and Wherein the two or more actual optical images comprise two or more actual optical images corresponding to different values of a parameter of an optical tool, two or more actual optical images of a layer on the specimen generated before and after a process is performed on the specimen, two or more actual optical images of different physical layers on the specimen, two or more actual optical images of the specimen generated by different optical tools, two or more actual optical images of the specimen corresponding to different values of a parameter of a process performed on the specimen, or a combination thereof.

15. The system of claim 1 , wherein the information for the specimen further comprises two or more actual electron beam images of the specimen, and wherein the two or more actual electron beam images comprise two or more actual electron beam images corresponding to different values of a parameter of an electron beam tool, two or more actual electron beam images of a layer on the specimen generated before and after a process is performed on the specimen, two or more actual electron beam images of different physical layers on the specimen, two or more actual electron beam images of the specimen generated by different electron beam tools, two or more actual electron beam images of the specimen corresponding to different values of a parameter of a process performed on the specimen, or a combination thereof.

16. The system of claim 1 , wherein the design data for the specimen comprises the design data stacked by: different selected patterns in the same layer, different layers, different materials, or a combination thereof.

17. The system of claim 1 , wherein the information for the specimen further comprises information for patterned features formed on the specimen, and wherein the information for the patterned features is generated experimentally or theoretically.

18. The system of claim 1 , wherein the learning based model comprises a discriminative model.

19. The system of claim 18 , wherein the discriminative model comprises a support vector machine, a support vector regression, a convolutional neural network, or a recurrent neural network.

20. The system of claim 1 , wherein the learning based model comprises a parametric or non-parametric Bayesian appr

Assignees

• Kla Tencor Corp
• Kla Tencor Corp

Inventors

• Bhaskar Kris
• Zhang Jing
• Chen Grace Hsiu-Ling
• Kulkarni Ashok
• Karsenti Laurent

Classifications

• G03F7/7065

  Defects, e.g. optical inspection of patterned layer for defects · CPC title

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title

• G03F1/84 Primary

  Inspecting · CPC title

• G03F1/86

  by charged particle beam [CPB] · CPC title

• G06T2207/10061

  from scanning electron microscope · CPC title