Pattern selection for lithographic model calibration
First Claim
1. A computer-implemented method of test pattern selection for computational lithography model calibration, comprising:
- identifying a pool of candidate test patterns;
identifying a set of lithography model parameters;
identifying a p-dimensional sensitivity space associated with the identified set of lithography model parameters, wherein each dimension in the p-dimensional sensitivity space corresponds to one parameter of the identified set of lithography model parameters, and wherein each dimension in the p-dimensional sensitivity space is substantially orthogonal to all other p−
1 dimensions in the p-dimensional sensitivity space;
calculating sensitivity variations along one or more dimensions in the p-dimensional sensitivity space associated with the set of test patterns; and
automatically selecting, by the computer, a set of test patterns from the identified pool of candidate test patterns that are most effective in determining optimal values of the identified set of lithography model parameters based on the calculated sensitivity variations associated with the set of test patterns.
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Abstract
The present invention relates generally to methods and apparatuses for test pattern selection for computational lithography model calibration. According to some aspects, the pattern selection algorithms of the present invention can be applied to any existing pool of candidate test patterns. According to some aspects, the present invention automatically selects those test patterns that are most effective in determining the optimal model parameter values from an existing pool of candidate test patterns, as opposed to designing optimal patterns. According to additional aspects, the selected set of test patterns according to the invention is able to excite all the known physics and chemistry in the model formulation, making sure that the wafer data for the test patterns can drive the model calibration to the optimal parameter values that realize the upper bound of prediction accuracy imposed by the model formulation.
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Citations
12 Claims
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1. A computer-implemented method of test pattern selection for computational lithography model calibration, comprising:
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identifying a pool of candidate test patterns; identifying a set of lithography model parameters; identifying a p-dimensional sensitivity space associated with the identified set of lithography model parameters, wherein each dimension in the p-dimensional sensitivity space corresponds to one parameter of the identified set of lithography model parameters, and wherein each dimension in the p-dimensional sensitivity space is substantially orthogonal to all other p−
1 dimensions in the p-dimensional sensitivity space;calculating sensitivity variations along one or more dimensions in the p-dimensional sensitivity space associated with the set of test patterns; and automatically selecting, by the computer, a set of test patterns from the identified pool of candidate test patterns that are most effective in determining optimal values of the identified set of lithography model parameters based on the calculated sensitivity variations associated with the set of test patterns. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. 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, which when executed, cause the computer to perform a method comprising:
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identifying a pool of candidate test patterns; identifying a set of lithography model parameters; identifying a p-dimensional sensitivity space associated with the identified set of lithography model parameters, wherein each dimension in the p-dimensional sensitivity space corresponds to one parameter of the identified set of lithography model parameters, and wherein each dimension in the p-dimensional sensitivity space is substantially orthogonal to all other p−
1 dimensions in the p-dimensional sensitivity space;calculating sensitivity variations along one or more dimensions in the p-dimensional sensitivity space associated with the set of test patterns; and automatically selecting, by the computer, a set of test patterns from the identified pool of candidate test patterns that are most effective in determining optimal values of the identified set of lithography model parameters based on the calculated sensitivity variations associated with the set of test patterns.
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Specification