Method for lithography model calibration

• US 7,488,933 B2
• Filed: 08/02/2006
• Issued: 02/10/2009
• Est. Priority Date: 08/05/2005
• Status: Active Grant

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: Brion Technologies Incorporated (ASML Holding NV)
• Inventors: Chen, Guangqing, Ye, Jun, Cao, Yu, Hunsche, Stefan
• Primary Examiner(s): Porta; David P
• Assistant Examiner(s): Gaworecki; Mark R
• Application Number:
  US11/461,929
• Publication Number:
  US 20070032896A1
• Time in Patent Office: 923 Days
• Field of Search: 250/252.1, 716/21
• US Class Current: 250/252.1
• 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

Abstract

A method for separately calibrating an optical model and a resist model of lithography process using information derived from in-situ aerial image measurements to improve the calibration of both the optical model and the resist model components of the lithography simulation model. Aerial images produced by an exposure tool are measured using an image sensor array loaded into the exposure tool. Multiple embodiments of measuring aerial image information and using the measured aerial image information to calibrate the optical model and the resist model are disclosed. The method of the invention creates more accurate and separable optical and resist models, leading to better predictability of the pattern transfer process from mask to wafer, more accurate verification of circuit patterns and how they will actually print in production, and more accurate model-based process control in the wafer fabrication facility.

Claims

1. A method for lithography model calibration, comprising:

selecting a model for a lithography process, the model including an optical model and a resist model, the optical model having initial values of optical model parameters and the resist model having initial values of resist model parameters;

measuring an aerial image using an image sensor array to produce a measured sensor image, the aerial image being produced by a lithography tool from a mask created using circuit design data;

simulating the lithography process using the optical model and the circuit design data to produce a simulated aerial image;

determining differences between the simulated aerial image and the measured sensor image; and

modifying the values of the optical model parameters based on the differences between the simulated aerial image and the measured sensor image to identify optimal values for the optical model parameters, wherein a simulated aerial image produced using the optimal values for the optical model parameters is an optimal simulated aerial image that is a best match for the measured sensor image.

2. The method of claim 1, further comprising:

processing a wafer using the lithography process and the mask to produce printed resist patterns;

measuring the printed resist patterns to produce measured resist patterns;

simulating formation of resist patterns on a wafer using the resist model and the optimal simulated aerial image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying the values of the resist model parameters based on the differences between the simulated resist patterns and the measured resist patterns to identify optimal values for the resist model parameters, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns.

3. The method of claim 2, wherein the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

4. The method of claim 2, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

5. The method of claim 1, wherein the optimal values for the optical model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

6. The method of claim 1, wherein the differences between the simulated aerial image and the measured sensor image include one or more values of image intensity, image slope, image contrast, critical dimension, line-end pullback, and corner rounding.

7. The method of claim 1, wherein the image sensor array is disposed in an image sensor unit that is capable of being loaded into the wafer stage of an exposure tool.

8. The method of claim 1, wherein the image sensor array is disposed in an image sensor unit that is integrated into an exposure tool.

9. The method of claim 1, wherein the circuit design data are a set of calibration patterns.

10. The method of claim 9, wherein the set of calibration patterns includes patterns covering a wide area of a pattern parameter space that are sensitive to process variations.

11. The method of claim 10, wherein the patterns have varying values of size and pitch.

12. The method of claim 9, wherein the set of calibration patterns includes line-space patterns, contacts, line-ends, and brick wall patterns.

13. The method of claim 9, wherein the set of calibration patterns includes line-space patterns with varying values of orientation, hexagonal patterns, or brick wall patterns for optimal calibration of optical aberration parameters.

14. The method of claim 1, wherein simulating the lithography process and measuring the aerial image are performed for a range of patterns, process window conditions, and optical conditions.

15. The method of claim 14, wherein the range of patterns includes one or more of line-space patterns with varying pitch and line-end patterns.

16. The method of claim 14, wherein the process window conditions include one or more varying values of focus and exposure dose.

17. The method of claim 14, wherein the optical conditions include one or more varying values of numerical aperture and partial coherence.

18. A method for calibrating a model of a lithography process, comprising:

selecting a model for a lithography process, the model including an optical model and a resist model, the optical model having initial values of optical model parameters;

selecting a sensor model for an image sensor array, the sensor model having initial values of sensor model parameters;

measuring an aerial image using the image sensor array to produce a measured sensor image, the aerial image being produced by a lithography tool from a mask created using circuit design data;

simulating behavior of the image sensor array using the optical model, the sensor model, and the circuit design data to produce a simulated sensor image;

determining differences between the measured sensor image and the simulated sensor image; and

modifying the optical model parameters and the sensor model parameters based on the differences between the measured sensor image and the simulated sensor image to identify optimal values for the optical model parameters and optimal values for the sensor model parameters, wherein a simulated sensor image produced using the optimal values for the optical model parameters and the optimal values for the sensor model parameters is an optimal simulated sensor image that is a best match for the measured sensor image.

19. The method of claim 18, further comprising:

processing a wafer using the lithography process and the mask to produce printed resist patterns;

measuring the printed resist patterns to produce measured resist patterns;

simulating the lithography process using the optical model with the optimal optical model parameters to produce an optimal simulated aerial image;

simulating formation of resist patterns on a wafer using the resist model and the optimal simulated aerial image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying the values of the resist model parameters based on the differences between the simulated resist patterns and the measured resist patterns to identify optimal values for the resist model parameters, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns.

20. The method of claim 19, wherein the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

21. The method of claim 19, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

22. The method of claim 18, wherein the optimal values for the optical model parameters and the optimal values for the sensor model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

23. The method of claim 18, wherein the differences between the measured sensor image and the simulated sensor image include one or more values of image intensity, image slope, image contrast, critical dimension, line-end pullback, and corner rounding.

24. The method of claim 18, wherein the image sensor array is disposed in an image sensor unit that is capable of being loaded into the wafer stage of an exposure tool.

25. The method of claim 18, wherein the image sensor array is disposed in an image sensor unit integrated into an exposure tool.

26. The method of claim 18, wherein the circuit design data are a set of calibration patterns.

27. The method of claim 26, wherein the set of calibration patterns includes patterns covering a wide area of a pattern parameter space that are sensitive to process variations.

28. The method of claim 27, wherein the patterns have varying values of size and pitch.

29. The method of claim 26, wherein the set of calibration patterns includes line-space patterns, contacts, line-ends, and brick wall patterns.

30. The method of claim 26, wherein the set of calibration patterns includes line-space patterns with varying values of orientation, hexagonal patterns, or brick wall patterns for optimal calibration of optical aberration parameters.

31. The method of claim 18, wherein simulating behavior of the image sensor array and measuring the aerial image are performed for a range of patterns, process window conditions, and optical conditions.

32. The method of claim 31, wherein the range of patterns includes one or more of line-space patterns with varying pitch and line-end patterns.

33. The method of claim 31, wherein the process window conditions include one or more varying values of focus and exposure dose.

34. The method of claim 31, wherein the optical conditions include one or more varying values of numerical aperture and partial coherence.

35. A method for calibrating a model of a lithography process, comprising:

selecting a model for a lithography process, the model including an optical model and a resist model, the optical model having initial values of optical model parameters;

selecting a sensor model for an image sensor array, the sensor model having initial values of sensor model parameters;

measuring an aerial image using the image sensor array to produce a measured sensor image, the aerial image being produced by a lithography tool from a mask created using circuit design data;

simulating the lithography process using the optical model and the circuit design data to produce a simulated aerial image;

simulating behavior of the image sensor array using the simulated aerial image and the sensor model to produce a simulated sensor image;

determining differences between the measured sensor image and the simulated sensor image; and

modifying the optical model parameters and the sensor model parameters based on the differences between the measured sensor image and the simulated sensor image to identify optimal values for the optical model parameters and optimal values for the sensor model parameters, wherein a simulated sensor image produced using the optimal values for the optical model parameters and the optimal values for the sensor model parameters is an optimal simulated sensor image that is a best match for the measured sensor image.

36. The method of claim 35, further comprising:

processing a wafer using the lithography process and the mask to produce printed resist patterns;

measuring the printed resist patterns to produce measured resist patterns;

simulating the lithography process using the optical model with the optimal optical model parameters to produce an optimal simulated aerial image;

simulating formation of resist patterns on a wafer using the resist model and the optimal simulated aerial image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying values of resist model parameters of the resist model based on the differences between the simulated resist patterns and the measured resist patterns to identify optimal values for the resist model parameters, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns.

37. The method of claim 36, wherein the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

38. The method of claim 36, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

39. The method of claim 35, further comprising:

processing a wafer using the lithography process and the mask to produce printed resist patterns;

measuring the printed resist patterns to produce measured resist patterns;

simulating formation of resist patterns on a wafer using the resist model and the optimal simulated sensor image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying values of resist model parameters of the resist model based on the differences between the simulated resist patterns and the measured resist patterns to identify optimal values for the resist model parameters, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns.

40. The method of claim 39, wherein the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

41. The method of claim 39, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

42. The method of claim 35, wherein the optimal values for the optical model parameters and the optimal values for the sensor model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

43. The method of claim 35, wherein the differences between the simulated aerial image and the measured sensor image include one or more values of image intensity, image slope, image contrast, critical dimension, line-end pullback, and corner rounding.

44. The method of claim 35, wherein the image sensor array is disposed in an image sensor unit that is capable of being loaded into the wafer stage of an exposure tool.

45. The method of claim 35, wherein the image sensor array is disposed in an image sensor unit integrated into an exposure tool.

46. The method of claim 35, wherein the circuit design data are a set of calibration patterns.

47. The method of claim 46, wherein the set of calibration patterns includes patterns covering a wide area of a pattern parameter space that are sensitive to process variations.

48. The method of claim 47, wherein the patterns have varying values of size and pitch.

49. The method of claim 46, wherein the set of calibration patterns includes line-space patterns, contacts, line-ends, and brick wall patterns.

50. The method of claim 46, wherein the set of calibration patterns includes line-space patterns with varying values of orientation, hexagonal patterns, or brick wall patterns for optimal calibration of optical aberration parameters.

51. The method of claim 35, wherein simulating the lithography process, simulating behavior of the image sensor array, and measuring the aerial image are performed for a range of patterns, process window conditions, and optical conditions.

52. The method of claim 51, wherein the range of patterns includes one or more of line-space patterns with varying pitch and line-end patterns.

53. The method of claim 51, wherein the process window conditions include one or more varying values of focus and exposure dose.

54. The method of claim 51, wherein the optical conditions include one or more varying values of numerical aperture and partial coherence.

55. A method for calibrating a model of a lithography process, comprising:

selecting a model for a lithography process, the model including an optical model and a resist model, the optical model having initial values of optical model parameters and the resist model having initial values of resist model parameters;

selecting an inverse sensor model for an image sensor array, the inverse sensor model having initial values of inverse sensor model parameters;

measuring an aerial image using the image sensor array to produce a measured sensor image, the aerial image being produced by a lithography tool from a mask created using circuit design data;

generating a reconstructed aerial image using the measured sensor image and the inverse sensor model;

simulating the lithography process using the optical model and the circuit design data to produce a simulated aerial image;

determining differences between the simulated aerial image and the reconstructed aerial image; and

modifying the optical model parameters and the inverse sensor model parameters based on the differences between the simulated aerial image and the reconstructed aerial image to identify optimal values for the optical model parameters and optimal values for the inverse sensor model parameters, wherein a simulated aerial image produced using the optimal values for the optical model parameters is an optimal simulated aerial image that is a best match for the reconstructed aerial image.

56. The method of claim 55, further comprising:

processing a wafer using the lithography process and the mask to produce printed resist patterns;

measuring the printed resist patterns to produce measured resist patterns;

simulating formation of resist patterns on a wafer using the resist model and the optimal simulated aerial image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying the values of the resist model parameters based on the differences between the simulated resist patterns and the measured resist patterns to identify optimal values for the resist model parameters, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns.

57. The method of claim 56, wherein the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

58. The method of claim 56, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

59. The method of claim 55, wherein the optimal values for the optical model parameters and the optimal values for the inverse sensor model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

60. The method of claim 55, wherein the differences between the simulated aerial image and the reconstructed aerial image include one or more values of image intensity, image slope, image contrast, critical dimension, line-end pullback, and corner rounding.

61. The method of claim 55, wherein the image sensor array is disposed in an image sensor unit capable of being loaded into the wafer stage of an exposure tool.

62. The method of claim 55, wherein the image sensor array is disposed in an image sensor unit that is integrated into an exposure tool.

63. The method of claim 55, wherein the circuit design data are a set of calibration patterns.

64. The method of claim 63, wherein the set of calibration patterns includes patterns covering a wide area of a pattern parameter space that are sensitive to process variations.

65. The method of claim 64, wherein the patterns have varying values of size and pitch.

66. The method of claim 63, wherein the set of calibration patterns includes line-space patterns, contacts, line-ends, and brick wall patterns.

67. The method of claim 63, wherein the set of calibration patterns includes line-space patterns with varying values of orientation, hexagonal patterns, or brick wall patterns for optimal calibration of optical aberration parameters.

68. The method of claim 55, wherein measuring the aerial image, generating the reconstructed aerial image, and simulating the lithography process are performed for a range of patterns, process window conditions, and optical conditions.

69. The method of claim 68, wherein the range of patterns includes one or more of line-space patterns with varying pitch and line-end patterns.

70. The method of claim 68, wherein the process window conditions include one or more varying values of focus and exposure dose.

71. The method of claim 68, wherein the optical conditions include one or more varying values of numerical aperture and partial coherence.

72. A method for calibrating a model of a lithography process, comprising:

selecting a model for a lithography process, the model including an optical model and a resist model, the optical model having initial values of optical model parameters and the resist model having initial values of resist model parameters;

selecting a sensor model for an image sensor array, the sensor model having values of sensor model parameters;

measuring an aerial image using the image sensor array to produce a measured sensor image, the aerial image being produced by a lithography tool from a mask created using circuit design data;

processing a wafer using the lithography process and the mask to produce printed resist patterns;

measuring the printed resist patterns to produce measured resist patterns;

simulating behavior of the image sensor array using the optical model, the circuit design data, and the sensor model to produce a simulated sensor image;

determining differences between the measured sensor image and the simulated sensor image;

simulating the lithography process using the optical model and the circuit design data to produce a simulated aerial image;

simulating formation of resist patterns on a wafer using the resist model and the simulated aerial image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying the values of the optical model parameters and the resist model parameters based on a total difference between the simulated resist patterns and the measured resist patterns as well as between the measured sensor image and the simulated sensor image to identify optimal values for the optical model parameters and optimal values for the resist model parameters simultaneously, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns, and wherein a simulated sensor image produced using the optimal values for the optical model parameters is an optimal simulated sensor image that is a best match for the measured sensor image.

73. The method of claim 72, wherein the optimal values for the optical model parameters and the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

74. The method of claim 72, wherein the differences between the simulated sensor image and the measured sensor image include one or more values of image intensity, image slope, image contrast, critical dimension, line-end pullback, and corner rounding.

75. The method of claim 72, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

76. The method of claim 72, wherein the image sensor array is disposed in an image sensor unit that is capable of being loaded into the wafer stage of an exposure tool.

77. The method of claim 72, wherein the image sensor array is disposed in an image sensor unit that is integrated into an exposure tool.

78. The method of claim 72, wherein the circuit design data are a set of calibration patterns.

79. The method of claim 78, wherein the set of calibration patterns includes patterns covering a wide area of a pattern parameter space that are sensitive to process variations.

80. The method of claim 79, wherein the patterns have varying values of size and pitch.

81. The method of claim 78, wherein the set of calibration patterns includes line-space patterns, contacts, line-ends, and brick wall patterns.

82. The method of claim 78, wherein the set of calibration patterns includes line-space patterns with varying values of orientation, hexagonal patterns, or brick wall patterns for optimal calibration of optical aberration parameters.

83. The method of claim 72, wherein simulating the lithography process, simulating behavior of the image sensor array, and measuring the aerial image are performed for a range of patterns, process window conditions, and optical conditions.

84. The method of claim 83, wherein the range of patterns includes one or more of line-space patterns with varying pitch and line-end patterns.

85. The method of claim 83, wherein the process window conditions include one or more varying values of focus and exposure dose.

86. The method of claim 83, wherein the optical conditions include one or more varying values of numerical aperture and partial coherence.

87. A computer-readable medium containing instructions for calibrating a model of a lithography process by performing:

storing a model for a lithography process, the model including an optical model and a resist model, the optical model having initial values of optical model parameters, and the resist model having initial values of resist model parameters;

storing a sensor model for an image sensor array, the sensor model having initial values of sensor model parameters;

receiving a measured sensor image produced by measuring an aerial image using the image sensor array, the aerial image produced by a lithography tool from a mask created using circuit design data;

simulating the lithography process using the optical model and the circuit design data to produce a simulated aerial image;

simulating behavior of the image sensor array using the simulated aerial image and the sensor model to produce a simulated sensor image;

determining differences between the simulated sensor image and the measured sensor image; and

modifying the optical model parameters and the sensor model parameters based on the differences between the simulated sensor image and the measured sensor image to identify optimal values for the optical model parameters and optimal values for the sensor model parameters, wherein a simulated sensor image produced using the optimal values for the optical model parameters and the optimal values for the sensor model parameters is an optimal simulated sensor image that is a best match for the measured sensor image.

88. The computer-readable medium of claim 87, containing further instructions for performing:

receiving measured resist patterns of resist patterns printed onto a wafer using the lithography process and the mask;

simulating the lithography process using the optical model with the optimal optical model parameters to produce an optimal simulated aerial image;

simulating formation of resist patterns on a wafer using the resist model and the optimal simulated aerial image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying values of resist model parameters of the resist model based on the differences between the simulated resist patterns and the measured resist patterns to identify optimal values for the resist model parameters, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns.

89. The computer-readable medium of claim 88, wherein the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

90. The computer-readable medium of claim 88, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

91. The computer-readable medium of claim 87, containing further instructions for performing:

receiving measured resist patterns of resist patterns printed onto a wafer using the lithography process and the mask;

simulating formation of resist patterns on a wafer using the resist model and the optimal simulated sensor image to produce simulated resist patterns;

determining differences between the simulated resist patterns and the measured resist patterns; and

modifying the values of the resist model parameters based on the differences between the simulated resist patterns and the measured resist patterns to identify optimal values for the resist model parameters, wherein simulated resist patterns produced using the optimal values for the resist model parameters are optimal simulated resist patterns that are a best match for the measured resist patterns.

92. The computer-readable medium of claim 91, wherein the optimal values for the resist model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

93. The computer-readable medium of claim 91, wherein the differences between the simulated resist patterns and the measured resist patterns include one or more values of critical dimension, resist thickness, sidewall angle, resist profile, footing, and top-profile rounding.

94. The computer-readable medium of claim 87, wherein the optimal values for the optical model parameters and the optimal values for the sensor model parameters are identified using one or more of a set of fitting algorithms, the set of fitting algorithms including minimizing Root-Mean-Square (RMS) errors, removing outliers, minimizing maximum residual errors, and using weighting factors to preferentially fit specific points, regions, metrics, or optical conditions.

95. The computer-readable medium of claim 87, wherein the differences between the simulated sensor image and the measured sensor image include one or more values of image intensity, image slope, image contrast, critical dimension, line-end pullback, and corner rounding.

96. The computer-readable medium of claim 87, wherein the circuit design data are a set of calibration patterns.

97. The computer-readable medium of claim 96, wherein the set of calibration patterns includes patterns covering a wide area of a pattern parameter space that are sensitive to process variations.

98. The computer-readable medium of claim 97, wherein the patterns have varying values of size and pitch.

99. The computer-readable medium of claim 96, wherein the set of calibration patterns includes line-space patterns, contacts, line-ends, and brick wall patterns.

100. The computer-readable medium of claim 96, wherein the set of calibration patterns includes line-space patterns with varying values of orientation, hexagonal patterns, or brick wall patterns for optimal calibration of optical aberration parameters.

101. The computer-readable medium of claim 87, wherein simulating the lithography process and measuring the aerial image are performed for a range of patterns, process window conditions, and optical conditions.

102. The computer-readable medium of claim 101, wherein the range of patterns includes one or more of line-space patterns with varying pitch and line-end patterns.

103. The computer-readable medium of claim 101, wherein the process window conditions include one or more varying values of focus and exposure dose.

104. The computer-readable medium of claim 101, wherein the optical conditions include one or more varying values of numerical aperture and partial coherence.