Browsing by Author "Hsu, Ya-Chen"

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An Effective Procedure for Calculating Weibull Production Yield with Mean Shift
(2012-05-01)
Hsu, Ya-Chen
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Pearn, W. L.
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Li, Yuan-Yi
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工業工程與管理學系
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Department of Industrial Engineering and Management
Motorola, Inc. introduced its six sigma production quality initiative to the world in the 1980s. Six sigma production quality is estimated by assuming a 1.5 sigma shift to the process mean. Bothe provided a statistical reason for considering such a shift in the normal process mean, and he also provided a much more accurate capability calculation to measure the production yield (Bothe, D. R., "Statistical Reason for the 1.5 sigma Shift," Qual. Eng., Vol. 14(3), 2002, pp. 479-487). In this paper, we consider Weibull processes, which cover a wide class of non-normal processes. We calculate the mean shift adjustments under various sample sizes n and Weibull parameters, with the power fixed at 0.5. Then, we implement the derived results to develop an effective procedure to accurately calculate the production yield. Finally, to demonstrate the applicability of the proposed approach, the proposed procedure is applied to a real manufacturing process with mean shift.
Capability adjustment or gamma processes with mean shift consideration in implementing Six Sigma program
(2008-12-01)
Hsu, Ya-Chen
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Pearn, W. L.
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Wu, Pei-Ching
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工業工程與管理學系
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Department of Industrial Engineering and Management
In the 1980s, Motorola, Inc. introduced its Six Sigma quality program to the world. Some quality practitioners questioned why the Six Sigma advocates claim it is necessary to add a 1.5 sigma shift to the process mean when estimating process capability. Bothe [Bothe, D.R., 2002. Statistical reason for the 1.5 sigma shift. Quality Engineering 14 (3), 479-487] provided a statistical reason for considering such a shift in the process mean for normal processes. In this paper, we consider gamma processes which cover a wide class of applications. For fixed sample size n, the detection power of the control chart can be computed. For small process mean shifts, it is beyond the control chart detection power, which results in overestimating process capability. To resolve the problem, we first examine Bothe's approach and find the detection power is less than 0.5 when data comes from gamma distribution, showing that Bothe's adjustments are inadequate when we have gamma processes. We then calculate adjustments under various sample sizes n and gamma parameter N, with power fixed to 0.5. At the end, we adjust the formula of process capability to accommodate those shifts which can not be detected. Consequently, our adjustments provide much more accurate capability calculation for gamma processes. For illustration purpose, an application example is presented. (C) 2007 Elsevier B. V. All rights reserved.
Optimal tool replacement for processes with low fraction defective
(2007-08-01)
Pearn, W. L.
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Hsu, Ya-Chen
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工業工程與管理學系
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Department of Industrial Engineering and Management
Tool wear is a frequent and natural part in many machining processes and is a systematic assignable cause. The fraction of defectives would rise as the tool deteriorates. When the fraction defective reaches a certain level, the tool must be replaced. To minimize the defective parts and the overall tool costs, the optimal tool replacement time needs to be determined. Process capability indices (PCIs) have been effectively used in the manufacturing industry to measure the fraction of defectives. Conventional methods of capability measurement become inaccurate since the process data is contaminated by the assignable cause variation. In order to determine the optimal tool replacement time to maintain maximum product quality, conventional capability calculation must be modified. Considering process capability changes dynamically, an estimator of C-pmk is investigated. We obtain an exact form of the sampling distribution in the presence of a systematic assignable cause. This study provides an effective management policy for optimal tool replacement under low fraction of defectives. To illustrate the application of this procedure, a case study involving the tool wear problem is presented. (c) 2006 Elsevier B.V. All rights reserved.
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
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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
;
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.
Tool Replacement for Photovoltaic Industry Processes With Multiple Characteristics Based on Capability Index
(2015-09-01)
Hsu, Ya-Chen
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Jane, Amber
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Pearn, W. L.
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工業工程與管理學系
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Department of Industrial Engineering and Management
In the photovoltaic industry, measuring efficiency is an important process for manufacturing silicon wafers. The cutting tool gradually wears during the measuring efficiency process. Consequently, the fraction of defectives would gradually become significant. When the fraction defective reaches a certain level, the tool must be replaced. Therefore, it is essential to find out the optimal time to replace the cutting tool for minimizing the fraction defective and reducing the manufacturing cost. In this research, we consider the replacement problem in the photovoltaic industry to obtain the optimal replacement time for the measuring efficiency process. The measuring efficiency process involves multiple product quality characteristics, including the I-sc, V-oc, and R-sh. In order to maintain the high wafer process quality and to minimize the production cost, we first develop a method for measuring the manufacturing capability of measuring efficiency process. Then, a tool replacement policy is proposed to assess the true capability at each time period of measuring efficiency process and to find out the optimal time for tool replacement.