Abstract
This paper presents a comprehensive and successful application of data mining methodologies to improve wafer yield in a semiconductor wafer fabrication system. To begin with, this paper applies a clustering method to automatically identify AUF (Area Uniform Failure) phenomenon from data instead of visual inspection that bad chips occurs in a specific area of wafer. Next, sequential pattern analysis and classification methods are applied to find out machines and parameters that are cause of low yield, respectively. Finally, this paper demonstrates an information system, Y2R-PLUS (Yield Rapid Ramp-up, Prediction, analysis & Up Support) that is developed in order to analyze wafer yield in a Korea semiconductor manufacturer.
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© 2005 Springer-Verlag Berlin Heidelberg
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Baek, DH., Jeong, IJ., Han, C.H. (2005). Application of Data Mining for Improving Yield in Wafer Fabrication System. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_25
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DOI: https://doi.org/10.1007/11424925_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25863-6
Online ISBN: 978-3-540-32309-9
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