Description Usage Arguments Value Author(s) References See Also Examples

Function to compute upper Clopper-Pearson confidence limits of failure probabilities on the basis of burn-in studies with countermeasures for each subset of a chip. Optionally, the required number of additional inspections for reaching a predefined target failure probability with countermeasures is returned.

1 | ```
ci.syn.cm(k, n, K, theta, alpha = 0.1, p.target = 1, tol = 1e-10)
``` |

`k` |
vector of numbers of failures for each subset. |

`n` |
vector of numbers of inspections for each subset. |

`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 |

`theta` |
vector of (different) effectivenesses of countermeasures. |

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

`p.target` |
target failure probability (optional). |

`tol` |
tolerance of |

`p.hat.cm` |
upper Clopper-Pearson confidence limit of the failure probability of the assembled devices with countermeasures. |

`n.add.cm` |
required number of additional inspections of each subset for reaching |

Daniel Kurz, Horst Lewitschnig

Maintainer: Horst Lewitschnig horst.lewitschnig@infineon.com

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
#Subset 1: no failures.
#Subset 2: 1 failure - failure tackled with 80% efficiency.
k<-c(0,1)
K<-matrix(c(0,1),2,1,byrow=TRUE)
theta<-0.8
n<-c(110000,330000)
ci.syn.cm(k,n,K,theta,0.1,20e-06)
#Subset 1: 1 failure - failure tackled with 80% efficiency.
#Subset 2: 1 failure - failure tackled with 70% efficiency.
#Subset 3: 2 failures - 1 failure tackled with 80%,
#1 failure with 70% efficiency.
k<-c(1,1,2)
K<-matrix(c(1,0,0,1,1,1),3,2,byrow=TRUE)
theta<-c(0.8,0.7)
n<-c(110000,150000,220000)
ci.syn.cm(k,n,K,theta,0.1,20e-06)
#Subset 1: 1 failure - failure tackled with 80% efficiency.
#Subset 2: 1 failure - failure without countermeasure.
#Subset 3: 2 failures - 1 failure tackled with 70% efficiency,
#1 failure without countermeasure.
k<-c(1,1,2)
K<-matrix(c(1,0,0,0,0,1),3,2,byrow=TRUE)
theta<-c(0.8,0.7)
n<-c(110000,150000,220000)
ci.syn.cm(k,n,K,theta,0.1,20e-06)
``` |

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