AdvBinomApps-package {AdvBinomApps}

Upper Clopper-Pearson Confidence Limits For Burn-in Studies under Additional Available Information

Description

Functions to compute upper Clopper-Pearson confidence limits of early life failure probabilities and required sample sizes of burn-in studies under further available information, e.g. from other products or technologies.

Details

Package:	AdvBinomApps
Type:	Package
Version:	1.0
Date:	2016-04-05
License:	GPL-3

Author(s)

Daniel Kurz, Horst Lewitschnig

Maintainer: Horst Lewitschnig [e-mail]

References

C.J. Clopper and E.S. Pearson: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26(4): 404-413, 1934.

D. Kurz, H. Lewitschnig and J. Pilz: Decision-Theoretical Model for Failures Tackled by Countermeasures. IEEE Transactions on Reliability, 63(2): 583-592, 2014. DOI: 10.1109/TR.2014.2315952.

D. Kurz, H. Lewitschnig and J. Pilz: Failure probability estimation under additional subsystem information with application to semiconductor burn-in. Resubmitted to: Journal of Applied Statistics, 2015.

D. Kurz, H. Lewitschnig and J. Pilz: An Advanced Area Scaling Approach for Semiconductor Burn-in. Microelectronics Reliability, 55(1): 129-137, 2015. DOI: 10.1016/j.microrel.2014.09.007.

D. Kurz, H. Lewitschnig and J. Pilz: Failure Probability Estimation with Differently Sized Reference Products for Semiconductor Burn-in Studies. Applied Stochastic Models in Business and Industry, 31(5): 732-744, 2015. DOI: 10.1002/asmb.2100.

Acknowledgment

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 fuer Informationstechnologie" by the Austrian Ministry for Transport, Innovation and Technology.

See Also

GenBinomApps

Examples

#MULTIPLE REFERENCE PRODUCTS:
k<-c(1,2)
n<-c(110000,138000)
A.ref<-c(5.21,10.71)
A.follow<-8.5
p.target<-20e-06
ci.mult.ref(k,n,A.ref,A.follow,p.target=p.target)

#SYNERGIES
k<-c(0,1)
n<-c(110000,330000)
ci.syn(k,n,0.1,20e-06)

#SEPARATE AREA SCALING
k<-c(1,0)
n<-110000
A.ref<-c(1,1.5)
A.follow<-c(1,2)
p.target<-20e-06
ci.sas(k,n,A.ref,A.follow,0.1,p.target)