改进的切线空间距离度量用于光刻热点的精确分类

Jing Guo, Fan Yang, S. Sinha, C. Chiang, Xuan Zeng
{"title":"改进的切线空间距离度量用于光刻热点的精确分类","authors":"Jing Guo, Fan Yang, S. Sinha, C. Chiang, Xuan Zeng","doi":"10.1145/2228360.2228577","DOIUrl":null,"url":null,"abstract":"A distance metric of patterns is crucial to hotspot cluster analysis and classification. In this paper, we propose an improved tangent space based metric for pattern matching based hotspot cluster analysis and classification. The proposed distance metric is an important extension of the well-developed tangent space method in computer vision. It can handle patterns containing multiple polygons, while the traditional tangent space method can only deal with patterns with a single polygon. It inherits most of the advantages of the traditional tangent space method, e.g., it is easy to compute and is tolerant with small variations or shifts of the shapes. Compared with the existing distance metric based on XOR of hotspot patterns, the improved tangent space based distance metric can achieve up to 37.5% accuracy improvement with at most 4.3× computational cost in the context of cluster analysis. The improved tangent space based distance metric is a more reliable and accurate metric for hotspot cluster analysis and classification. It is more suitable for industry applications.","PeriodicalId":263599,"journal":{"name":"DAC Design Automation Conference 2012","volume":"138 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Improved tangent space based distance metric for accurate lithographic hotspot classification\",\"authors\":\"Jing Guo, Fan Yang, S. Sinha, C. Chiang, Xuan Zeng\",\"doi\":\"10.1145/2228360.2228577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A distance metric of patterns is crucial to hotspot cluster analysis and classification. In this paper, we propose an improved tangent space based metric for pattern matching based hotspot cluster analysis and classification. The proposed distance metric is an important extension of the well-developed tangent space method in computer vision. It can handle patterns containing multiple polygons, while the traditional tangent space method can only deal with patterns with a single polygon. It inherits most of the advantages of the traditional tangent space method, e.g., it is easy to compute and is tolerant with small variations or shifts of the shapes. Compared with the existing distance metric based on XOR of hotspot patterns, the improved tangent space based distance metric can achieve up to 37.5% accuracy improvement with at most 4.3× computational cost in the context of cluster analysis. The improved tangent space based distance metric is a more reliable and accurate metric for hotspot cluster analysis and classification. It is more suitable for industry applications.\",\"PeriodicalId\":263599,\"journal\":{\"name\":\"DAC Design Automation Conference 2012\",\"volume\":\"138 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DAC Design Automation Conference 2012\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2228360.2228577\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DAC Design Automation Conference 2012","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2228360.2228577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27

摘要

模式的距离度量是热点聚类分析和分类的关键。本文提出了一种改进的基于切线空间的度量,用于基于模式匹配的热点聚类分析和分类。所提出的距离度量是计算机视觉中成熟的切空间方法的重要扩展。它可以处理包含多个多边形的图案,而传统的切空间方法只能处理单个多边形的图案。它继承了传统切空间方法的大部分优点,例如易于计算,并且可以容忍形状的微小变化或移位。与现有的基于热点模式异或的距离度量相比,改进的基于切线空间的距离度量在聚类分析中准确率提高了37.5%,计算成本最高为4.3倍。改进的切线空间距离度量是一种更可靠、更准确的热点聚类分析和分类度量。更适合工业应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved tangent space based distance metric for accurate lithographic hotspot classification
A distance metric of patterns is crucial to hotspot cluster analysis and classification. In this paper, we propose an improved tangent space based metric for pattern matching based hotspot cluster analysis and classification. The proposed distance metric is an important extension of the well-developed tangent space method in computer vision. It can handle patterns containing multiple polygons, while the traditional tangent space method can only deal with patterns with a single polygon. It inherits most of the advantages of the traditional tangent space method, e.g., it is easy to compute and is tolerant with small variations or shifts of the shapes. Compared with the existing distance metric based on XOR of hotspot patterns, the improved tangent space based distance metric can achieve up to 37.5% accuracy improvement with at most 4.3× computational cost in the context of cluster analysis. The improved tangent space based distance metric is a more reliable and accurate metric for hotspot cluster analysis and classification. It is more suitable for industry applications.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信