Method and apparatus for assessing the quality of a process model

• US 20060236297A1
• Filed: 10/03/2005
• Published: 10/19/2006
• Est. Priority Date: 03/17/2005
• Status: Active Grant

• Current Assignee: Synopsys Incorporated
• Original Assignee: Synopsys Incorporated
• Inventors: Yan, Qiliang, Melvin, Lawrence S. III
• Granted Patent: US 7,496,880 B2
• Time in Patent Office: Days
• US Class Current: 700/108
• CPC Class Codes: G03F 1/36 Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes
• CPC Class Codes: G03F 1/68 Preparation processes not covered by groups G03F1/20 - G03F1/50

Abstract

One embodiment of the present invention provides a system that assesses the quality of a process model. During operation, the system receives a mask layout and additionally receives a process model that models the effects of one or more semiconductor manufacturing processes on the mask layout. Next, the system computes a gradient of the process model with respect to a process model parameter. The system then computes a quality indicator at an evaluation point in the mask layout using the gradient of the process model and the mask layout. Next, the system assesses the quality of the process model using the quality indicator. In one embodiment, the system assesses the quality of the process model by comparing the quality indicator with a threshold.

Claims

1. A method for assessing the quality of a process model, the method comprising:

receiving a mask layout;

receiving a process model which models the effects of one or more semiconductor manufacturing processes on the mask layout;

computing a gradient of the process model with respect to a process model parameter;

computing a quality indicator at an evaluation point in the mask layout using the gradient of the process model and the mask layout; and

assessing the quality of the process model using the quality indicator.

2. The method of claim 1, wherein prior to receiving the process model, the method further comprises:

receiving a test layout;

obtaining process data by applying the one or more semiconductor manufacturing processes to the test layout; and

fitting a statistical model to the process data to obtain the process model.

3. The method of claim 2, wherein the process model can be represented using a set of kernel functions and a set of coefficients associated with the kernel functions.

4. The method of claim 1, wherein computing the quality indicator involves convolving the gradient of the process model with the mask layout.

5. The method of claim 2,

wherein assessing the quality of the process model involves comparing the quality indicator with a threshold;

wherein if the quality indicator is below a first threshold, the process model is determined to be normally sensitive to the process model parameter, which indicates that the process model is of a high quality; and

wherein if the quality indicator is above a second threshold, the process model is determined to be overly sensitive to the process model parameter, which indicates that the process model is of a low quality.

6. The method of claim 5, wherein if the quality indicator is above second threshold, the method further comprises refitting the statistical model to the process data to obtain a second process model that is of a better quality than the process model.

7. The method of claim 1, wherein the method is used to identify a high quality process model, which is subsequently used by an optical proximity correction (OPC) process.

8. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for assessing the quality of a process model, the method comprising:

receiving a mask layout;

receiving a process model which models the effects of one or more semiconductor manufacturing processes on the mask layout;

computing a gradient of the process model with respect to a process model parameter;

computing a quality indicator at an evaluation point in the mask layout using the gradient of the process model and the mask layout; and

assessing the quality of the process model using the quality indicator.

9. The computer-readable storage medium of claim 8, wherein prior to receiving the process model, the method further comprises:

receiving a test layout;

obtaining process data by applying the one or more semiconductor manufacturing processes to the test layout; and

fitting a statistical model to the process data to obtain the process model.

10. The computer-readable storage medium of claim 9, wherein the process model can be represented using a set of kernel functions and a set of coefficients associated with the kernel functions.

11. The computer-readable storage medium of claim 8, wherein computing the quality indicator involves convolving the gradient of the process model with the mask layout.

12. The computer-readable storage medium of claim 9, wherein assessing the quality of the process model involves comparing the quality indicator with a threshold;

wherein if the quality indicator is below a first threshold, the process model is determined to be normally sensitive to the process model parameter, which indicates that the process model is of a high quality; and

wherein if the quality indicator is above a second threshold, the process model is determined to be overly sensitive to the process model parameter, which indicates that the process model is of a low quality.

13. The computer-readable storage medium of claim 12, wherein if the quality indicator is above the second threshold, the method further comprises refitting the statistical model to the process data to obtain a second process model that is of a better quality than the process model.

14. The computer-readable storage medium of claim 8, wherein the method is used to identify a high quality process model, which is subsequently used by an optical proximity correction (OPC) process.

15. An apparatus for assessing the quality of a process model, comprising:

a first receiving mechanism configured to receive a mask layout;

a second receiving mechanism configured to receive a process model which models the effects of one or more semiconductor manufacturing processes on the mask layout;

a first computing mechanism configured to compute a gradient of the process model with respect to a process model parameter;

a second computing mechanism configured to compute a quality indicator at an evaluation point in the mask layout using the gradient of the process model and the mask layout; and

an assessment mechanism configured to assess the quality of the process model using the quality indicator.

16. The apparatus of claim 15, wherein prior to receiving the process model, the method further comprises:

a receiving mechanism configured to receive a test layout;

an obtaining mechanism configured to obtain process data by applying the one or more semiconductor manufacturing processes to the test layout; and

a fitting mechanism configured to fit a statistical model to the process data to obtain the process model.

17. The apparatus of claim 16, wherein the process model can be represented using a set of kernel functions and a set of coefficients associated with the kernel functions.

18. The apparatus of claim 15, wherein computing the quality indicator involves convolving the gradient of the process model with the mask layout.

19. The apparatus of claim 16,

wherein assessing the quality of the process model involves comparing the quality indicator with a threshold;

wherein if the quality indicator is below a first threshold, the process model is determined to be normally sensitive to the process model parameter, which indicates that the process model is of a high quality; and

wherein if the quality indicator is above a second threshold, the process model is determined to be overly sensitive to the process model parameter, which indicates that the process model is of a low quality.

20. The apparatus of claim 19, wherein if the quality indicator is above the second threshold, the method further comprises refitting the statistical model to the process data to obtain a second process model that is of a better quality than the process model.

21. The apparatus of claim 15, wherein the method is used to identify a high quality process model, which is subsequently used by an optical proximity correction (OPC) process.