An Overview on Recent Advances in Statistical Burn-In Modeling for Semiconductor Devices

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 231)

Abstract

In semiconductor manufacturing, the early life of the produced devices can be simulated by means of burn-in. In this way, early failures are screened out before delivery. To reduce the efforts associated with burn-in, the failure probability p in the early life of the devices is evaluated using a burn-in study. Classically, this is done by computing the exact Clopper–Pearson upper bound for p. In this chapter, we provide an overview on a series of new statistical models, which are capable of considering further available information (e.g., differently reliable chip areas) within the Clopper–Pearson estimator for p. These models help semiconductor manufacturers to more efficiently evaluate the early life failure probabilities of their products and therefore reduce the efforts associated with burn-in studies of new technologies.

Keywords

Area scaling Binomial distribution Burn-in Power semiconductors Sampling 

Notes

Acknowledgements

The work has been performed in the project EPT300, co-funded by grants from Austria, Germany, Italy, The Netherlands and the ENIAC Joint Undertaking. This project is co-funded within the programme “Forschung, Innovation und Technologie für Informationstechnologie” by the Austrian Ministry for Transport, Innovation and Technology.

References

  1. 1.
    Barlow, R., Proschan, F.: Statistical Theory of Reliability and Life Testing. Holt, Renerhart & Winston, New York (1975)MATHGoogle Scholar
  2. 2.
    Berg, B.A.: Clopper-Pearson bounds from HEP data cuts. AIP Conf. Proc. 583, 104–106 (2001)CrossRefGoogle Scholar
  3. 3.
    Brown, L.D., Cai, T.T., DasGupta, A.: Interval estimation for a binomial proportion. Stat. Sci. 16, 101–133 (2001)MathSciNetMATHGoogle Scholar
  4. 4.
    Brown, L.D., Cai, T.T., DasGupta, A.: Confidence intervals for a binomial proportion and asymptotic expansions. Ann. Stat. 30, 160–201 (2002)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934)CrossRefGoogle Scholar
  6. 6.
    Gerstle, D., Lee, P.: Impact of burn-in on power supply reliability. Power Electron. Technol. 20–25 (2005)Google Scholar
  7. 7.
    Jensen, F., Petersen, N.E.: Burn-In. Wiley, New York (1982)Google Scholar
  8. 8.
    Kececioglu, D., Sun, F.: Burn-in Testing - Its Quantification and Optimization. Prentice Hall, New Jersey (1997)Google Scholar
  9. 9.
    Kuo, W., Kuo, Y.: Facing the headaches of early failures: a state-of-the-art review of burn-in decisions. Proc. IEEE. 71, 1257–1266 (1983)CrossRefGoogle Scholar
  10. 10.
    Kuo, W., Chien, W.T.K., Kim, T.: Reliability, Yield, and Stress Burn-in. Kluwer Academic Publishers, Norwell, MA (1998)CrossRefGoogle Scholar
  11. 11.
    Kurz, D., Lewitschnig, H.: AdvBinomApps. R-package. http://cran.r-project.org/web/packages/AdvBinomApps. (Cited 3 May 2016)
  12. 12.
    Kurz, D., Lewitschnig, H., Pilz, J.: Decision-theoretical model for failures tackled by countermeasures. IEEE Trans. Reliab. 63, 583–592 (2014)CrossRefGoogle Scholar
  13. 13.
    Kurz, D., Lewitschnig, H., Pilz, J.: Survey of recent advanced statistical models for early life failure probability assessment in semiconductor manufacturing. Proc. Winter Sim. Conf. 2600–2608 (2014)Google Scholar
  14. 14.
    Kurz, D., Lewitschnig, H., Pilz, J.: Failure probability estimation with differently sized reference products for semiconductor burn-in studies. Appl. Stoch. Model. Bus. 31, 732–744 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kurz, D., Lewitschnig, H., Pilz, J.: An advanced area scaling approach for semiconductor burn-in. Microelectron. Reliab. 55, 129–137 (2015)CrossRefGoogle Scholar
  16. 16.
    Kurz, D., Lewitschnig, H., Pilz, J.: Failure probability estimation under additional subsystem information with application to semiconductor burn-in. J. Appl. Stat. 44, 955–967 (2017)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lewitschnig, H., Lenzi, D.: GenBinomApps. R-package. http://cran.r-project.org/web/packages/GenBinomApps. (Cited 3 May 2016)
  18. 18.
    Ooi, M.P.-L., Kassim, Z.A., Demidenko, S.N.: Shortening burn-in test: application of HVST and Weibull statistical analysis. IEEE Trans. Instrum. Meas. 56, 990–999 (2007)CrossRefGoogle Scholar
  19. 19.
    Reliability Edge: Guidelines for burn-in justification and burn-in time determination. ReliaSoft. 7 (2007). http://reliasoft.com/newsletter/v7i2/burn_in.htm. (Cited 4 May 2016)
  20. 20.
    Sullivan, A.K., Raben, D., Reekie, J., Rayment, M., Mocroft, A., et. al.: Feasibility and Effectiveness of Indicator Condition-Guided Testing for HIV: Results from HIDES I (HIV Indicator Diseases across Europe Study). PLoS ONE. 8, e52845 (2013)CrossRefGoogle Scholar
  21. 21.
    Ward, L.G., Heckman, M.G., Warren, A.I., Tran, K.: Dosing accuracy of insulin aspart flexpens after transport through the pneumatic tube system. Hosp. Pharm. 48, 33–38 (2013)CrossRefGoogle Scholar
  22. 22.
    Wilkins, D.J.: The Bathtub curve and product failure behavior. HotWire. 21 (2002). http://weibull.com/hotwire/issue21/hottopics21.htm. (Cited 3 May 2016)
  23. 23.
    Zakaria, F., Kassim, Z.A., Ooi, M.P.L., Demidenko, S.: Reducing burn-in time through high-voltage stress test and Weibull statistical analysis. IEEE Des. Test. Comput. 23, 88–98 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsAlpen-Adria University of Klagenfurt, Universitätsstrasse 65-67KlagenfurtAustria
  2. 2.Infineon Technologies Austria AGVillachAustria

Personalised recommendations