SEMICONDUCTOR DEVICES

The storage lifetime model based on multi-performance degradation parameters

Haochun Qi1, 2, , Xiaoling Zhang1, Xuesong Xie1 and Changzhi Lü1

+ Author Affiliations

 Corresponding author: Qi Haochun, Email:jetqi@sina.com

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Abstract: According to the multi-performance degradation of the bipolar transistor in the accelerating storage process, an extrapolation model of the storage lifetime is proposed. In this model, using the Wiener process simulates the mono-degradation process of each feature degradation; using the copula function describes the correlation among these feature degradations. The Wiener process and parameters in the copula function are considered to associate with the temperature, and their relationships can be represented by the converted equations. Through the maximum likelihood estimation, the parameters in the Wiener process can be found; introducing Kendall's tau, those in the copula function can be estimated. By conducting the regression analyses of the estimated values of the parameters in each stress, their corresponding converted equations can be shown. Based on the storage test data of bipolar transistors, with the estimation method, the storage lifetime is found. The findings show that the model is reasonable for the prediction of storage lifetime.

Key words: semiconductor device reliabilitylifetime estimationprediction methods



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Wang X, Nair V. A class of degradation model based on nonhomogeneous Gaussian process. University of Michigan, 2005
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Lee M Y, Tang J. A modified EM-algorithm for estimating the parameters of inverse gaussian distribution based on TIMS-censored wiener degradation data. Statistica Sinica, 2007, 17:873 doi: 10.1007%2F978-981-4451-98-7_4.pdf
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Sari J K. Multivariate degradation modeling and its application to reliability testing. Singapore: Department of Industrial and Systems Engineering, National University of Singapore, 2007
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Nelsen R B. An introduction to copulas. 2nd. New York:Springer Science Business Media, 2006
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Cox D R, Miller H D. The theory of stochastic processes. London: Chapman and Hall, 1965
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Christian G, Johanna N, Noomen B G. Estimators based on Kendall's tau in multivariate copula models. Australian & New Zealand Journal of Statistics 2011, 53(2):157 https://www.infona.pl/resource/bwmeta1.element.elsevier-a6993bbd-dcd1-378e-9a7b-7c5c59bffd1d
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Doksum K A, Hoyland A. Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution. Technometrics, 1992, 34(1):74 doi: 10.2307/1269554
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Qi Haochun, Lü Changzhi, Zhang Xiaoling, et al. Accelerating the life of transistors. Journal of Semiconductors, 2013, 34(6):064010 doi: 10.1088/1674-4926/34/6/064010
Fig. 1.  Degradation process in varying stress.

Fig. 2.  The relationship between $\ln \widehat{\sigma_2^2}$ and $1/s$.

Fig. 3.  The relationship between $\tau_2$ and $1/s$.

[1]
Huang W, Askin R G. Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation. Quality and Reliability Engineering International, 2003, 19:241 doi: 10.1002/(ISSN)1099-1638
[2]
Bagdonavicius V, Bikelis A, Kazakevicius V, et al. Analysis of joint multiple failure mode and linear degradation data with renewals. Journal of Statistical Planning Inference, 2007, 137:2191 doi: 10.1016/j.jspi.2006.07.002
[3]
Liang Z, Jun Y, Ling H F, et al. Reliability assessment based on multivariate degradation measures and competing failure analysis. Modern Appl Sci, 2011, 5(6):232 http://www.oalib.com/paper/2462767
[4]
Liu Jingjing, Sun Junjun, Hu Haiyun, et al. The life prediction for materials under the corrosion of seawater. Acta Phys Sin, 2005, 54(5):2414 https://www.researchgate.net/publication/296570934_The_life_prediction_for_materials_under_the_corrosion_of_seawater
[5]
Zhang Yongjin, Wang Zhongzhi. Cumulative damage model and parameter estimate about a kind of time-sharing redundant system. Acta Phys Sin, 2009, 58(9):6074 http://cn.bing.com/academic/profile?id=222d3765967686c412f2fca5e8a85de0&encoded=0&v=paper_preview&mkt=zh-cn
[6]
Lei Xiaoyi, Liu Hongxia, Zhang Kai, et al. Hot carrier degradation and a new lifetime prediction model in ultra-deep sub-micron pMOSFET. Chin Phys B, 2013, 22(4):047304 doi: 10.1088/1674-1056/22/4/047304
[7]
Chen Chengju, Zhang Xiaoling, Zhao Li. Storage life assessment of transistors based on accelerated degradation. Semicond Technol, 2013, 38(7):551 https://www.researchgate.net/publication/262076832_Storage_life_of_power_switching_transistors_based_on_performance_degradation_data
[8]
Yang D G, Kengen M, Peels W G M. Reliability modeling on a MOSFET power package based on embedded die technology. Microelectron Reliab, 2010, 50:923 doi: 10.1016/j.microrel.2010.02.026
[9]
Gebraeel N Z, Lawley M A. A neural network degradation model for computing and updating residual life distributions. IEEE Trans Automation Science and Engineering, 2008, 5(1):154 doi: 10.1109/TASE.2007.910302
[10]
Wu J, Deng C, Shao X, et al. A reliability assessment method based on support vector machines for CNC equipment. Science in China Series E:Technological Sciences, 2009, 52(7):1849 doi: 10.1007/s11431-009-0208-z
[11]
Wang X, Nair V. A class of degradation model based on nonhomogeneous Gaussian process. University of Michigan, 2005
[12]
Lee M Y, Tang J. A modified EM-algorithm for estimating the parameters of inverse gaussian distribution based on TIMS-censored wiener degradation data. Statistica Sinica, 2007, 17:873 doi: 10.1007%2F978-981-4451-98-7_4.pdf
[13]
Zhao Jianyin, Liu Fang, Sun Quan, et al. On line reliability estimation and performance prediction for metallized film pulse capacitor. Acta Armamentarii, 2006, 27(2):265 http://en.cnki.com.cn/Article_en/CJFDTOTAL-BIGO200602017.htm
[14]
Sari J K. Multivariate degradation modeling and its application to reliability testing. Singapore: Department of Industrial and Systems Engineering, National University of Singapore, 2007
[15]
Whitmore G A, Schenkelberg F. Modeling accelerated degradation data using wiener diffusion with a time scale transformation. Lifetime Data Analysis, 1997, 3(1):27 doi: 10.1023/A:1009664101413
[16]
Nelsen R B. An introduction to copulas. 2nd. New York:Springer Science Business Media, 2006
[17]
Cox D R, Miller H D. The theory of stochastic processes. London: Chapman and Hall, 1965
[18]
Christian G, Johanna N, Noomen B G. Estimators based on Kendall's tau in multivariate copula models. Australian & New Zealand Journal of Statistics 2011, 53(2):157 https://www.infona.pl/resource/bwmeta1.element.elsevier-a6993bbd-dcd1-378e-9a7b-7c5c59bffd1d
[19]
Doksum K A, Hoyland A. Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution. Technometrics, 1992, 34(1):74 doi: 10.2307/1269554
[20]
Qi Haochun, Lü Changzhi, Zhang Xiaoling, et al. Accelerating the life of transistors. Journal of Semiconductors, 2013, 34(6):064010 doi: 10.1088/1674-4926/34/6/064010
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    Received: 12 February 2014 Revised: 23 March 2014 Online: Published: 01 October 2014

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      Haochun Qi, Xiaoling Zhang, Xuesong Xie, Changzhi Lü. The storage lifetime model based on multi-performance degradation parameters[J]. Journal of Semiconductors, 2014, 35(10): 104012. doi: 10.1088/1674-4926/35/10/104012 H C Qi, X L Zhang, X S Xie, C Z Lü. The storage lifetime model based on multi-performance degradation parameters[J]. J. Semicond., 2014, 35(10): 104012. doi:  10.1088/1674-4926/35/10/104012.Export: BibTex EndNote
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      Haochun Qi, Xiaoling Zhang, Xuesong Xie, Changzhi Lü. The storage lifetime model based on multi-performance degradation parameters[J]. Journal of Semiconductors, 2014, 35(10): 104012. doi: 10.1088/1674-4926/35/10/104012

      H C Qi, X L Zhang, X S Xie, C Z Lü. The storage lifetime model based on multi-performance degradation parameters[J]. J. Semicond., 2014, 35(10): 104012. doi:  10.1088/1674-4926/35/10/104012.
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      The storage lifetime model based on multi-performance degradation parameters

      doi: 10.1088/1674-4926/35/10/104012
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      • Corresponding author: Qi Haochun, Email:jetqi@sina.com
      • Received Date: 2014-02-12
      • Revised Date: 2014-03-23
      • Published Date: 2014-10-01

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