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首页> 外文期刊>Journal of the Chinese Institute of Industrial Engineers >Data mining for yield enhancement in TFT-LCD manufacturing: an empirical study TFT-LCD * (*: cfchien@mx.nthu.edu.tw) View all notes 300 101333 189
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Data mining for yield enhancement in TFT-LCD manufacturing: an empirical study TFT-LCD * (*: cfchien@mx.nthu.edu.tw) View all notes 300 101333 189

机译：数据挖掘以提高TFT-LCD制造的产量：一项经验研究TFT-LCD *（*：cfchien@mx.nthu.edu.tw）查看所有说明300 101333 189

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摘要

The lengthy manufacturing processes of thin film transistor-liquid crystal displays (TFT-LCDs) are complex, in which many factors can cause different types of defects on the panel and result in low yield. Examples are line defects, point defects, and Mura defects. Engineers rely on personal experience for trouble shooting during TFT-LCD manufacture, which does not quickly locate possible fault root causes using their own domain knowledge or rules of thumb. In a fully automated manufacturing environment in TFT-LCD factories, large amounts of raw data are increasingly accumulated from various sources, automatically or semi-automatically, for fault diagnosis and process monitoring. This study aims to propose a data mining framework for diagnosing the root causes of defects in factories. The extracted information and knowledge is helpful to engineers as a basis for trouble shooting and defect diagnosis. To examine the validity of this approach, an empirical study was conducted in a TFT-LCD company in Taiwan, and the results demonstrated the practical viability of this approach. TFT-LCD (Array) (Cell) (Module) TFT-LCD View full textDownload full textKeywordsyield enhancement, data mining, rough set theory, decision tree, TFT-LCD manufacturingKeywords TFT-LCD Related var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10170660903541856
机译：薄膜晶体管-液晶显示器（TFT-LCD）的漫长的制造过程是复杂的，其中许多因素可导致面板上的不同类型的缺陷并导致低良率。示例包括线缺陷，点缺陷和Mura缺陷。工程师依靠个人经验来进行TFT-LCD制造过程中的故障排除，而这无法使用他们自己的领域知识或经验法则快速找到可能的故障根本原因。在TFT-LCD工厂的全自动生产环境中，越来越多的原始数据越来越多地从各种来源自动或半自动地收集起来，用于故障诊断和过程监控。这项研究旨在提出一个数据挖掘框架，用于诊断工厂中缺陷的根本原因。所提取的信息和知识对于工程师进行故障排除和缺陷诊断很有帮助。为了检验这种方法的有效性，在台湾的一家TFT-LCD公司进行了一项实证研究，结果证明了这种方法的实际可行性。 TFT-LCD（阵列）（单元）（模块）TFT-LCD查看全文下载关键字产量提高，数据挖掘，粗糙集理论，决策树，TFT-LCD制造关键字与TFT-LCD相关的关键字var addthis_config = {ui_cobrand：“泰勒和弗朗西斯在线”，services_compact：“ citeulike，netvibes，twitter，technorati，可口，linkedin，facebook，stumbleupon，digg，google，更多”，发布号：“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10170660903541856

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