发明名称 |
Extraction of imaging parameters for computational lithography using a data weighting algorithm |
摘要 |
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). |
申请公布号 |
US8806388(B2) |
申请公布日期 |
2014.08.12 |
申请号 |
US201313849227 |
申请日期 |
2013.03.22 |
申请人 |
Texas Instruments Incorporated |
发明人 |
Parikh Ashesh |
分类号 |
G06F17/50;G03F7/20;G03F1/36 |
主分类号 |
G06F17/50 |
代理机构 |
|
代理人 |
Conser Eugene C.;Telecky, Jr. Frederick J. |
主权项 |
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, 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. |
地址 |
Dallas TX US |