Test set identification for improved delay defect coverage in the presence of statistical delays

Javvaji, Pavan Kumar; Shanyour, Basim; Tragoudas, Spyros

doi:10.1109/isqed.2018.8357258

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Therefore, conventional approaches for testing reach their limitations and new methods must be investigated and developed [9]. Conventional fault models and test method may not be able to effectively handle such marginal defects with a small delay [10].…”

Section: Introductionmentioning

confidence: 99%

Retraction:Reliability assurance in early-life-failure test through improved nearest neighbor regression

Song

Liang

Huang

et al. 2020

IEICE Electron. Express

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During manufacturing test, researchers usually overlook the importance of process variation defects and marginal defects, which can seriously affect test results of Early-Life-Failure (ELF). Theoretically, machine learning classification methods can be used to identify these two defects. In fact, when features overfitting or data distribution overlap seriously, classifiers perform poorly, it will not achieve the desired results. This paper first-ever proposes a kind of data preprocessing method combines improved K-Nearest Neighbors (KNN) regression classifier, so that the classification results will be enhanced in terms of classification performance. Experiment results demonstrate that the predictive accuracy is 45% higher than the traditional logistic regression method. This proposed method will drive critical new requirements for fault modeling, test generation and test application, and implementing them effectively will require a new level of collaboration between process and product developers.

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Section: Introductionmentioning

confidence: 99%