AdvBinomApps (version 1.0)

ci.mult.ref: Upper Clopper-Pearson confidence limits for area scaling with differently sized reference products

Description

Function to compute upper Clopper-Pearson confidence limits of failure probabilities on the basis of differently sized reference products. Optionally, the required numbers of additional inspections for each reference product to reach a predefined target failure probability of the follower product are returned.

Usage

ci.mult.ref(k, n, A.ref, A.follow, alpha = 0.1, p.target = 1, 
prec = 2, tailcut = 1e-08, tol = 1e-12)

Arguments

k

vector of total numbers of failures for each reference product.

n

vector of numbers of inspected devices for each reference product.

A.ref

vector of chip sizes for each reference product (in mm^2).

A.follow

size of follower product.

alpha

alpha-level (1-alpha confidence level, default: 0.1).

p.target

target failure probability of follower product (optional).

prec

precision for greatest common divisor is 10^-prec (default: 2).

tailcut

probabilities for scaled failures smaller than tailcut are set to zero for each reference product (default: 1e-08). Too small values for tailcut might cause increased computation times.

tol

tolerance of uniroot-function used for computing failure probability per greatest common chip size (default: 1e-12).

Value

p.ref

vector of upper Clopper-Pearson confidence limits for each reference product (without the other reference products).

p.mm

upper Clopper-Pearson confidence limit of the failure probability per mm^2 (on the basis of all reference products).

p.follow

upper Clopper-Pearson confidence limit of the failure probability of the follower product (on the basis of all reference products).

n.add

vector of required numbers of additional inspections for each reference product in order to reach p.target for the follower product.

References

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.

See Also

phi.mult.ref ci.mult.ref.cm phi.mult.ref.cm

Examples

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)

k<-c(1,2,2)
n<-c(110000,138000,170000)
A.ref<-c(5.21,10.71,7.89)
A.follow<-8.5
p.target<-20e-06
ci.mult.ref(k,n,A.ref,A.follow,p.target=p.target)