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The yield estimation of semiconductor products based ontruncated samples

Published online by Cambridge University Press:  06 March 2014

K. Gu ,
X.Z. Jia ,
H.L. You  and
T. Liang
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Abstract

Product yield reflects the potential product quality and reliability, which means thathigh yield corresponds to good quality and high reliability. Yet consumers usuallycouldn’t know the actual yield of the products they purchase. Generally, the products thatconsumers get from suppliers are all eligible. Since the quality characteristic of theeligible products is covered by the specifications, then the observations of qualitycharacteristic follow truncated normal distribution. In the light of maximum likelihoodestimation, this paper proposes an algorithm for calculating the parameters of fullGaussian distribution before truncation based on truncated data and estimating productyield. The confidence interval of the yield result is derived, and the effect of samplesize on the precision of the calculation result is also analyzed. Finally, theeffectiveness of this algorithm is verified by an actual instance.

Keywords

Truncated normal distributionproduct qualityproduct yieldmaximum likelihood estimatefisher information matrix
Type
Research Article
Information
International Journal of Metrology and Quality Engineering , Volume 4 , Issue 3 , 2013 , pp. 215 - 220
Copyright
© EDP Sciences 2014

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References

T. Pyzdek, P. Keller, The Six Sigma Handbook, 3rd edn. (McGraw-Hill Professional, New York, 2010)
Mo, P.H., Corruption and economic growth, J. Comp. Econom. 29, 66–79 (2001) CrossRefGoogle Scholar
Pearn, W.L. et al., Testing process precision for truncated normal distributions, Microelectron. Reliab. 47, 2275–2281 (2007) CrossRefGoogle Scholar
Kane, V.E., Process capability indices, J. Qual. Technol. 18, 41–52 (1986) Google Scholar
Kotz, S., Johnson, N.L., Process capability indices – A review, 1992–2000, J. Qual. Technol. 34, 1–19 (2002) Google Scholar
Wu, C.W., Pearn, W.L., Samuel, K., An overview of theory and practice on process capability indices for quality assurance, Int. J. Prod. Econ. 117, 338–359 (2009) CrossRefGoogle Scholar
S. Rose-Ackerman, Redesigning the State to Fight Corruption: Transparency, Competition and Privatization (Public Policy for Private Sector, World Bank, Washington, DC, 1996)
Chao, M.-T., Lin, D.K.J., Another Look at the Process Capability Index, Qual. Reliab. Eng. Int. 22, 153–163 (2006) CrossRefGoogle Scholar
L. Norman Johnson, S. Kotz, N. Balakrishnan, Continuous Univariate Distributions, 2nd edn. (Wiley-Interscience, New York, 1994), Vol. 1
J.-F. Bonnans, J.C. Gilbert, C. Lemarechal, Numerical optimization: Theoretical and practical aspects, 2nd edn. (Springer-Verlag, Berlin, 2006)
Edgeworth, F.Y., On the Probable Errors of Frequency-Constants, J. Roy. Stat. Soc. 71, 499–512 (1908) CrossRefGoogle Scholar
Pratt, J.W., Edgeworth, F.Y., Fisher, R.A., On the Efficiency of Maximum Likelihood Estimation, Ann. Stat. 4, 501–514 (1976) CrossRefGoogle Scholar
W.K. Newey, D. McFadden, Handbook of Econometrics (Elsevier Science, Amsterdam, 1994), Vol. 4