Variation and failure characterization through pattern classification of test data from multiple test stages

Hsu, C. S.; Sarson, Peter; Schatzberger, Gregor; Leisenberger, Friedrich Peter; Carulli, John M.; Siddhartha, Siddhartha; Cheng, Kwang-Ting

doi:10.1109/test.2016.7805845

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…This shift undermines the effectiveness of a simple stuck-at based test solution. Data-based classification algorithms have improved continuously with the change of defects, minimize yield loss [11,12,13,14,15,16,17], ML can be used to distinguish between marginal defects and process variation defects based on circuit delay, depend on different delay distribution. The classification results can be able to locate the defects and identify the root cause.…”

Section: Introductionmentioning

confidence: 99%

“…As we can see, most of previous works are based on conventional fault models [11,12,13,14,15,16,17,18], lack of research on marginal defects and process variation defects with machine learning classification methods. To bridge this gap and formally show that, in a quest to reduce ELF and increase reliability, the work presented in this paper combines the concepts of regression and KNN algorithm with data preprocessing method.…”

Section: Introductionmentioning

confidence: 99%

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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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