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    National Tsing Hua University Institutional Repository > 工學院  > 工業工程與工程管理學系 > 會議論文  >  Using process experienced correlation table to improve the accuracy and reliability of data mining for yield improvement


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/46428


    Title: Using process experienced correlation table to improve the accuracy and reliability of data mining for yield improvement
    Authors: Haw-Jyue Luo;Wang, S.R.;Chen, C.C.;Hung-En Tai;Chen-Fu Chien;Pei-Nong Chen
    教師: 簡禎富
    Date: 2004
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: 2004 Semiconductor Manufacturing Technology Workshop Proceedings, Hsinchu, Taiwan, 10-10 Sept. 2004, pages 127 - 130
    Keywords: correlation methods
    data analysis
    data mining
    integrated circuit yield
    semiconductor device manufacture
    statistical analysis
    Abstract: The rapid innovation of new process technologies in the semiconductor industry, especially 12 inches Fab, along with continuously growing amounts of data, it is difficult to find root cause when problems occur in some process steps. It causes large amount of wafer scrapping. The analysis methods of traditional EDA system rely on experience of senior engineers. They need to define the suspected process step by their experience and then perform analysis. The analysis methods consume large amounts of human resources in order to determine the root cause of process and yield excursions. Hence, it is important that a knowledge retention method be incorporated to improve the efficiency of root cause analysis. Data mining, a new data analysis method that combines information science and technology of statistical analysis, is developed recently. The new generation data analysis method includes statistical, information science and mathematical calculation to find correlation between the target parameter, for example yield and other parameters. It will provide important clue to the analyzer. In addition, it also provides a direction to find root cause rapidly. It is difficult to find the correlation between the target parameter and other parameters by traditional statistical analysis method, and data mining can solve the blind point of the traditional method. This article discusses the design of how to define the relation between all data sources of semiconductor industry based on the experience of senior engineers. And it installs the relation to data mining analysis, it performs the analysis to identify relationship among all data sources. So, engineers can find the root cause of process issue in a short period of time
    URI: http://www.ieee.org/
    http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/46428
    Appears in Collections:[工業工程與工程管理學系] 會議論文

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