Yen-Ting Yu, Y. Chan, S. Sinha, I. Jiang, C. Chiang
{"title":"Accurate process-hotspot detection using critical design rule extraction","authors":"Yen-Ting Yu, Y. Chan, S. Sinha, I. Jiang, C. Chiang","doi":"10.1145/2228360.2228576","DOIUrl":null,"url":null,"abstract":"In advanced fabrication technology, the sub-wavelength lithography gap causes unwanted layout distortions. Even if a layout passes design rule checking (DRC), it still might contain process hotspots, which are sensitive to the lithographic process. Hence, process-hotspot detection has become a crucial issue. In this paper, we propose an accurate process-hotspot detection framework. Unlike existing DRC-based works, we extract only critical design rules to express the topological features of hotspot patterns. We adopt a two-stage filtering process to locate all hotspots accurately and efficiently. Compared with state-of-the-art DRC-based works, our results show that our approach can reach 100% success rate with significant speedups.","PeriodicalId":263599,"journal":{"name":"DAC Design Automation Conference 2012","volume":"218 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"77","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DAC Design Automation Conference 2012","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2228360.2228576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 77
Abstract
In advanced fabrication technology, the sub-wavelength lithography gap causes unwanted layout distortions. Even if a layout passes design rule checking (DRC), it still might contain process hotspots, which are sensitive to the lithographic process. Hence, process-hotspot detection has become a crucial issue. In this paper, we propose an accurate process-hotspot detection framework. Unlike existing DRC-based works, we extract only critical design rules to express the topological features of hotspot patterns. We adopt a two-stage filtering process to locate all hotspots accurately and efficiently. Compared with state-of-the-art DRC-based works, our results show that our approach can reach 100% success rate with significant speedups.