Browsing by Author "Chuang, Ya-Fei"

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Precision Tool Condition Monitoring for Grinding Wheel in IC Manufacturing of Silicon Wafer
(2012-11-01)
Hsu, Ya-Chen
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Pearn, W. L.
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Chuang, Ya-Fei
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工業工程與管理學系
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Department of Industrial Engineering and Management
In integrated circuit (IC) manufacturing industries, wafer grinding is an important step because it improves wafer flatness and thickness. The diamond-grinding wheel used for grinding the wafers is expensive. Inevitably, the diamond-grinding wheel wears gradually during the grinding process, and the wafer surface is contaminated by grinding chips. Consequently, the fraction of defectives gradually becomes significant. In this situation, cost reduction is important for silicon wafer suppliers in the global market. Therefore, it is essential to monitor the grinding wheel in order to reduce the manufacturing cost and minimize the fraction of defectives. In this paper, we consider the grinding-wheel replacement problem in IC factories with the aim of determining the optimal replacement time for the grinding wheels. In order to maintain high wafer process quality and minimize the production cost, we propose a tool condition monitoring procedure for assessing the true capability at each time period of the grinding process and then find the optimal time for tool replacement. The procedure can be used practically for the silicon wafer manufacturing industry.
Sample size determination for production yield estimation with multiple independent process characteristics
(2009-08-01)
Hsu, Ya-Chen
;
Pearn, W. L.
;
Chuang, Ya-Fei
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Capability measure for processes yield with single characteristic has been investigated extensively, but is still comparatively neglected for processes with multiple characteristics. Wu and Pearn [Wu, C.W., Pearn, W.L. 2005. Measuring manufacturing capability for couplers and wavelength division multiplexers (WDM). International journal of Advanced Manufacturing Technology 25(5/6), 533-541] proposed a capability index for multiple characteristics called C(PU)(T), which provides an exact measure on process yield for multiple characteristics with each characteristic normally distributed. However, the exact sampling distribution of C(PU)(T)(multiple characteristics) is analytically intractable. In this paper, we apply the bootstrap method for calculating the lower confidence bounds of the index C(PU)(T), and determine the sample size for a specified estimation accuracy. In order to obtain a desired estimation quality assurance, the sample size determination is essential as it provides the accuracy of the lower bound obtained from the bootstrap method. For convenience of applications, we tabulate the sample size required for various designated accuracy for the engineers/practitioners to use. A real-world example from manufacturing process with multiple characteristics is investigated to illustrate the applicability of the proposed approach. (C) 2008 Published by Elsevier B.V.