Extraction of imaging parameters for computational lithography using a data weighting algorithm
First Claim
1. A method of computational lithography, comprising:
- collecting an inline critical dimension (CD) data set including CD data obtained from printing a test structure having resist on a substrate using a mask including a set of gratings which provides a plurality of feature types including different ratios of line width to space width, said printing including a range of different focus values;
weighting said CD data, using a computing device, to form a weighted CD data set using a weighting algorithm (WA) that assigns cost weights to said CD data based on a feature type of said plurality of feature types and a magnitude of a variation of its CD value with respect to a CD value for said feature type at a nominal focus (nominal CD), said WA algorithm reducing a value of said cost weight as said magnitude of said variation increases;
extracting at least one imaging parameter, using said computing device, from said weighted CD data set, andusing said computing device, automatically calibrating a computational lithography model using said imaging parameter;
wherein said WA is an inverse weight algorithm (IWA), said IWA algorithm assigning said cost weights as an inverse proportion to said magnitude of said variation.
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Abstract
A method of computational lithography includes collecting a critical dimension (CD) data set including CD data from printing a test structure including a set of gratings which provide a plurality of feature types including different ratios of line width to space width, where the printing includes a range of different focus values. The CD data is weighted to form a weighted CD data set using a weighting algorithm (WA) that assigns cost weights to the CD data based its feature type and its magnitude of CD variation with respect to a CD value for its feature type at a nominal focus (nominal CD). The WA algorithm reduces a value of the cost weight as the magnitude of variation increases. At least one imaging parameter is extracted from the weighted CD data set. A computational lithography model is automatically calibrated using the imaging parameter(s).
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Citations
11 Claims
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1. A method of computational lithography, comprising:
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collecting an inline critical dimension (CD) data set including CD data obtained from printing a test structure having resist on a substrate using a mask including a set of gratings which provides a plurality of feature types including different ratios of line width to space width, said printing including a range of different focus values; weighting said CD data, using a computing device, to form a weighted CD data set using a weighting algorithm (WA) that assigns cost weights to said CD data based on a feature type of said plurality of feature types and a magnitude of a variation of its CD value with respect to a CD value for said feature type at a nominal focus (nominal CD), said WA algorithm reducing a value of said cost weight as said magnitude of said variation increases; extracting at least one imaging parameter, using said computing device, from said weighted CD data set, and using said computing device, automatically calibrating a computational lithography model using said imaging parameter;
wherein said WA is an inverse weight algorithm (IWA), said IWA algorithm assigning said cost weights as an inverse proportion to said magnitude of said variation. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer program product, comprising:
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executable code transportable by a non-transitory machine readable medium, wherein execution of said code by at least one programmable computer causes said programmable computer to perform a sequence of steps for a computational lithography process for a lithography system, said sequence of steps comprising; weighting data obtained from an inline critical dimension (CD) data set including CD data obtained from printing a test structure having resist on a substrate using a mask including a set of gratings which provides a plurality of feature types including different ratios of line width to space width, said printing including a range of different focus values, wherein said weighting uses a weighting algorithm (WA) that assigns cost weights to said CD data based on a feature type of said plurality of feature types and a magnitude of a variation of its CD value with respect to a CD value for said feature type at a nominal focus (nominal CD), said WA algorithm reducing a value of said cost weight as said magnitude of said variation increases; extracting at least one imaging parameter from a weighted CD data set, and automatically calibrating a computational lithography model using said imaging parameter;
wherein said WA is an inverse weight algorithm (IWA), said IWA algorithm assigning said cost weights as an inverse proportion to said magnitude of said variation. - View Dependent Claims (8, 9, 10, 11)
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Specification