Information matrix creation and calibration test pattern selection based on computational lithography model parameters

• US 9,588,439 B1
• Filed: 12/20/2011
• Issued: 03/07/2017
• Est. Priority Date: 12/21/2010
• Status: Active Grant

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: ASML Netherlands BV (ASML Holding NV)
• Inventors: Bruguier, Antoine Jean, Cao, Yu, Ye, Jun, Shao, Wenjin
• Primary Examiner(s): Chad, Aniss
• Application Number:
  US13/332,303
• Time in Patent Office: 1,904 Days
• Field of Search: 703/2
• US Class Current: 1/1
• CPC Class Codes: G03F 7/705 Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions
• CPC Class Codes: G03F 7/70516 Calibration of components of the microlithographic apparatus, e.g. light sources, addressable masks or detectors
• CPC Class Codes: G03F 7/70625 Dimensions, e.g. line width, critical dimension [CD], profile, sidewall angle or edge roughness
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: H01L 21/027 Making masks on semiconductor bodies for further photolithographic processing not provided for in group H01L21/18 or H01L21/34 photographic masks or originals per se G03F1/00; registration or positioning of photographic masks or originals G03F9/00; photographic cameras G03B; control of position G05D3/00

Abstract

Embodiments of the present invention describe methods of selecting a subset of test patterns from an initial larger set of test patterns for calibrating a computational lithography model. An example method comprises: generating an information matrix for the initial larger set of test patterns, wherein the terms of the information matrix comprise one or more identified model parameters that represent a lithographic process response; and, executing a selection algorithm using terms of the information matrix to select the subset of test patterns that effectively determines values of the identified model parameters that contribute significantly in the lithographic process response, wherein the subset of test patterns characteristically represents the initial larger set of test patterns. The selection algorithm explores coverage relationships existing in the initial larger set of test patterns.

Claims

1. A method of selecting a subset of test patterns from an initial set of test patterns, the initial set of test patterns having a larger number of test patterns than the number of test patterns in the subset, the method comprising:

generating an information matrix for the initial set of test patterns, wherein the information matrix comprises an input value for each of a plurality of parameters for variable terms of a computational lithography model that models at least part of a process involving lithography, the input values determined for each of the test patterns in the initial set of test patterns; and

selecting, by a computer hardware system and based on the information matrix input values of the plurality of parameters determined for each of the initial set of test patterns, the subset of test patterns for calibrating the computational lithography model.

2. The method of claim 1, wherein the selecting explores coverage relationships existing in the initial set of test patterns.

3. The method of claim 1, wherein the input values are determined from a process response that is one or more selected from:

critical dimension and/or change in critical dimension of features of the test patterns, a resist image and/or change in resist image, an aerial image intensity and/or change in aerial image intensity when optical settings are changed in the model, and/or changes in aerial image intensity when transmission cross coefficients (TCCs) are tuned in the model.

4. The method of claim 1, wherein the selecting comprises:

identifying an enclosed region in a parametric space, the parametric space including data points representing the initial set of test patterns, wherein each dimension of the parametric space corresponds to a parameter extracted from the information matrix; and

selecting data points from within the enclosed region to determine the test patterns to be added to the subset of test patterns.

5. The method of claim 4, wherein the selecting data points comprises selecting data points by sampling uniformly within the enclosed region, thereby selecting the test patterns corresponding to or represented by the selected data points.

6. The method of claim 5, wherein the selection of data points is governed by predefined rules.

7. The method of claim 6, wherein the predefined rules are based on one or more selected from:

proximity from a determined effective center of gravity of a cluster of data points;

one or more predefined radii from a reference point in the parametric space; and

/or a predefined respective weighting factor associated with individual test patterns in the initial set of test patterns.

8. The method of claim 7, wherein the predefined rules are based on a predefined respective weighting factor associated with individual test patterns in the initial set of test patterns and wherein the weighting factor is related to a relative importance of a test pattern, experimental measurement quality, identification of a hot/warm spot during verification, or a combination thereof.

9. The method of claim 5, wherein the method further comprises:

performing a mathematical search function based on results of the sampling step to determine if too few or too many test patterns have been selected;

modifying the sampling step to iteratively select an intended number of test patterns.

10. The method of claim 5, wherein sampling uniformly includes selecting data points at extremes of the enclosed region in the parametric space.

11. A computer program product comprising one or more non-transitory computer-readable storage media having computer-executable instructions for causing a computer to select test patterns for calibrating a computational lithography model, the instructions causing the computer to perform the method of claim 4.

12. The computer program product of claim 11, wherein the selecting data points comprises selecting data points by sampling uniformly within the enclosed region, thereby selecting the test patterns corresponding to or represented by the selected data points.

13. The computer program product of claim 12, wherein the selection of data points is governed by predefined rules based on one or more of:

proximity from a determined effective center of gravity of a cluster of data points;

one or more predefined radii from a reference point in the parametric space; and

predefined respective weighting factors associated with individual test patterns in the initial set of test patterns.

14. The method of claim 1, wherein a process response represented by the model is a resist image (RI) in a plane (x,y), and the input values of the model parameters in the information matrix are related to the process response by the following equation,

15. A computer program product comprising one or more non-transitory computer-readable storage media having computer-executable instructions for causing a computer to select test patterns for calibrating a computational lithography model, the instructions causing the computer to perform the method of claim 14.

16. The method of claim 1, wherein the information matrix input values are generated by using received input values of the model parameters, wherein the received input values comprise estimates of the values that are supplied to a solver that accommodates the lithography model to generate expanded terms of the lithography model.

17. The method of claim 16, wherein, for a given test pattern and a given term of the model, the input values received are based on:

a first intensity term and a first slope term at a first point (xj, yj) on a wafer, and, a second intensity term and a second slope term at a second point (xj′

, yj′

) on the wafer, the two points being located on a resist contour.

18. A computer program product comprising one or more non-transitory computer-readable storage media having computer-executable instructions for causing a computer to select test patterns for calibrating a computational lithography model, the instructions causing the computer to perform the method of claim 1.

19. The computer program product of claim 18, wherein the selecting explores coverage relationships existing in the initial set of test patterns.

20. The computer program product of claim 18, wherein the input values are determined from a process response that is one or more selected from:

critical dimension and/or change in critical dimension of features of the test patterns, a resist image and/or change in resist image, an aerial image intensity and/or change in aerial image intensity when optical settings are changed in the model, and/or changes in aerial image intensity when transmission cross coefficients (TCCs) are tuned in the model.

21. The method of claim 1, wherein a linear solver calculates input values of linear coefficients, and a non-linear solver calculates input values of non-linear parameters for the information matrix, wherein the linear solver and the non-linear solver are chosen depending on the computational lithography model to be calibrated.

22. The method of claim 21, wherein the computational lithography model comprises a first-principle-based model, an empirical model, or a combination thereof.

23. The method of claim 1, wherein each test pattern of the initial set has a different characteristic feature correlated to one or more different parameters of the computational lithography model.

24. The method of claim 1, wherein the subset of test patterns effectively determines input values of the model parameters that contribute more than other test patterns in a process response, wherein the subset of test patterns characteristically represents the initial set of test patterns.