ci.sas.cm: Separate area scaling for upper Clopper-Pearson confidence...

In AdvBinomApps: Upper Clopper-Pearson Confidence Limits for Burn-in Studies under Additional Available Information

Description

Function to compute upper Clopper-Pearson confidence limits of failure probabilities of follower products by means of separate area scaling (SAS) with failures tackled by countermeasures. Furthermore, the validity of the SAS in comparison to the classical area scaling (CAS) is evaluated taking into account the implemented countermeasures. Optionally, the required numbers of additional inspections of the reference product in order to reach the predefined target failure probability of the follower product according to the CAS and SAS with countermeasures are returned.

Usage

ci.sas.cm(k, n, A.ref, A.follow, K, theta, alpha = 0.1, 
p.target = 1, atol = 1e-08)

Arguments

k	vector of total numbers of failures for each subset on reference product (total number of failures on reference product = sum of entries of k).
n	number of inspected devices in burn-in study of reference product.
A.ref	vector of sizes for each subset on reference product (in mm^2).
A.follow	vector of sizes for each subset on follower product (in mm^2).
K	matrix with entries K[j,i] denoting the number of failures in the j-th subset tackled with the i-th countermeasure. If two or more countermeasures have the same efficiency, they can be handled as one countermeasure for several failures. If the i-th countermeasure does not apply to the j-th subset, then set K[j,i]=0. If there is no countermeasure for a failure at all, then it does not need to be considered in K (the failure itself is already considered in k).
theta	vector of (different) effectivenesses of countermeasures.
alpha	alpha-level (1-alpha confidence level, default: 0.1).
p.target	target failure probability of follower product (optional).
atol	tolerance of multiroot-function used for computing p.sas (default: 1e-08). For further details, see description of multiroot-function.

Details

Function makes use of multiroot-function of the package rootSolve to solve non-linear equation system for the subset failure probabilities.

Appropriate starting values for multiroot are chosen automatically.

In case of non-convergence of multiroot-function, NA is returned.

Function designed and verified for number of subsets < 6.

Value

p.cas.cm	vector of upper Clopper-Pearson confidence limits of failure probabilities for each subset on reference product according to CAS with countermeasures.
p.sas.cm	vector of upper Clopper-Pearson confidence limits of failure probabilities for each subset on reference product according to SAS with countermeasures.
p.follow.cas.cm	upper Clopper-Pearson confidence limit of the failure probability of the follower product according to CAS with countermeasures.
p.follow.sas.cm	upper Clopper-Pearson confidence limit of the failure probability of the follower product according to SAS with countermeasures.
delta.cm	evidence factor against CAS with countermeasures.
n.add.cas.cm	required number of additional inspections of the reference product in order to reach p.target of the follower product according to CAS with countermeasures.
n.add.sas.cm	required number of additional inspections of the reference product in order to reach p.target of the follower product according to SAS with countermeasures.

Author(s)

Daniel Kurz, Horst Lewitschnig

Maintainer: Horst Lewitschnig [e-mail]

References

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

See Also

ci.sas

Examples

#Reference product: 1 failure.
#Subset 1: 1 failure - failure tackled with 80% efficiency.
#Subset 2: no failures.
k<-c(1,0)
K<-matrix(c(1,0),2,1)
n<-100000
A.ref<-c(1,1)
theta<-0.8
A.follow<-c(1,2)
p.target<-20e-06
ci.sas.cm(k,n,A.ref,A.follow,K,theta,0.1,p.target)

#Reference product: 3 failures.
#Subset 1: 2 failures - 1 failure tackled with 80%, 
#1 failure with 70% efficiency.
#Subset 2: 1 failure - failure tackled with 70% efficiency.
#Subset 3: no failures.
k<-c(2,1,0)
K<-matrix(c(1,1,0,1,0,0),3,2,byrow=TRUE)
n<-100000
A.ref<-c(2,3,4)
theta<-c(0.8,0.7)
A.follow<-c(1,2,3)
p.target<-20e-06
ci.sas.cm(k,n,A.ref,A.follow,K,theta,0.1,p.target)

#Reference product: 4 failures.
#Subset 1: 2 failures - 1 failure tackled with 80% efficiency, 
#1 failure without countermeasure.
#Subset 2: 1 failure - failure tackled with 70% efficiency.
#Subset 3: 1 failure - failure without countermeasure.
k<-c(2,1,1)
K<-matrix(c(1,0,0,1,0,0),3,2,byrow=TRUE)
n<-100000
A.ref<-c(2,3,4)
theta<-c(0.8,0.7)
A.follow<-c(1,2,3)
p.target<-20e-06
ci.sas.cm(k,n,A.ref,A.follow,K,theta,0.1,p.target)

AdvBinomApps documentation built on May 2, 2019, 3:29 p.m.