Using text mining to handle unstructured data in semiconductor manufacturing — Yan-Hsiu Liu

机译：使用文本挖掘来处理半导体制造中的非结构化数据-Yan-Hsiu Liu

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In the field of semiconductor manufacturing, people usually focus on the data of well-designed databases, such as values from tool sensors, inline metrology data, or WAT data. These data are well structured and easily handled by engineers for further analysis. For example, integration engineers can compared CD metrology data to WAT data to catch out the root cause of abnormal device current, or equipment engineers can check FDC data to judge PM success or not.

机译：在半导体制造领域，人们通常将注意力集中在精心设计的数据库的数据上，例如工具传感器的值，在线计量数据或WAT数据。这些数据结构合理，易于工程师进行进一步分析。例如，集成工程师可以将CD计量数据与WAT数据进行比较，以找出导致设备电流异常的根本原因，或者设备工程师可以检查FDC数据来判断PM成功与否。

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来源
《Joint 2015 e-Manufacturing amp; Design Collaboration Symposium 2015 and 2015 International Symposium on Semiconductor Manufacturing》|2015年|1-3|共3页
会议地点 Taipei(CT)
作者
Yan-Hsiu Liu; Ji Fu Kung; Lin James; Hsu Y.B.;
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作者单位

United Microelectron. Corp., Tainan, Taiwan;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
data mining; production engineering computing; semiconductor industry; text analysis; semiconductor manufacturing; text mining; unstructured data handling; Cleaning; Joints; Manufacturing; Metrology; Text mining; Text processing; Yttrium; bag-of-words model; document-term-matrix; text mining; tokenization;

机译：数据挖掘;生产工程计算;半导体行业;文本分析;半导体制造;文本挖掘;非结构化数据处理;清洗;接头;制造;计量学;文本挖掘;文本处理;钇;词袋模型;文档术语矩阵;文本挖掘;令牌化;;

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Using text mining to handle unstructured data in semiconductor manufacturing — Yan-Hsiu Liu

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