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dc.contributor.authorLi, Yimingen_US
dc.contributor.authorYu, Shao-Mingen_US
dc.contributor.authorLi, Yih-Langen_US
dc.date.accessioned2014-12-08T15:17:28Z-
dc.date.available2014-12-08T15:17:28Z-
dc.date.issued2009-03-01en_US
dc.identifier.issn0927-0256en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.commatsci.2008.04.031en_US
dc.identifier.urihttp://hdl.handle.net/11536/12656-
dc.description.abstractOptical lithography is one of the key technologies in semiconductor material and device fabrications. It is a process to transfer the layouts of desired pattern onto the wafers. However, the exposure on wafer has distortions due to the proximity effects. As the minimum feature sizes of explored samples continue to shrink, the mismatch between the pattern and the experimental result on wafer is significant. Corrections of mask patterns between the sample and post exposure result are thus necessary. Optical proximity correction (OPC) is the process of modifying the geometries of the layouts to compensate for the non-ideal properties of the lithography process. Given the shapes desired on the wafer, the mask is modified to improve the reproduction of the critical geometry. In this work, we propose an intelligent OPC technique for process distortion compensation of layout mask. To perform the mask correction in sub-wavelength era, two different strategies including the genetic algorithm (GA) with model-based OPC and the GA with rule-based OPC methods are examined. The proposed intelligent system consists of three parts: the preprocess, the OPC engine, and the post-process. During the pre-process, the pattern analyzer will analysis all patterns and then divided them into many segments for model-based OPC or generates assistant patterns for rule-based OPC. Secondly, the OPC module is applied to correct the mask. The intelligent module searches the whole problem domain to find out the best combination of the mask shape by the GA. The corrected mask is verified by performing lithographic simulation to get the error norm between exposed result and desired layout. Finally, the mask verification is conducted in the post-process. By testing on several fundamental patterns, this approach shows good correction accuracy and efficiency, compared with experimentally fabricated samples. It can be applied to perform the mask correction in sub-wavelength era. (C) 2008 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectOptical proximity correctionen_US
dc.subjectLithographyen_US
dc.subjectGenetic algorithmen_US
dc.subjectNumerical simulationen_US
dc.subjectRule baseen_US
dc.subjectModel baseen_US
dc.titleIntelligent optical proximity correction using genetic algorithm with model- and rule-based approachesen_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.doi10.1016/j.commatsci.2008.04.031en_US
dc.identifier.journalCOMPUTATIONAL MATERIALS SCIENCEen_US
dc.citation.volume45en_US
dc.citation.issue1en_US
dc.citation.spage65en_US
dc.citation.epage76en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電信工程研究所zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentInstitute of Communications Engineeringen_US
dc.identifier.wosnumberWOS:000264463800010-
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