Important test selection for screening potential customer returns

Sumikawa, Nik; Drmanac, Dragoljub; Wang, Li-C.; Winemberg, LeRoy; Abadir, Magdy S.

doi:10.1109/vdat.2011.5783603

Cited by 14 publications

(10 citation statements)

References 6 publications

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“…The tests with the highest distances will be chosen for the final multivariate technique in that reduced space [6,7]. This method presents the major disadvantage to need this first bivariate analysis.…”

Section: Test Selection By Outlying Rankingmentioning

confidence: 99%

Study and implementation conditions of the multivariate outlier detection methods for screening of potential field failures

Bergès¹,

Wu²,

Soufflet³

2015

2015 IEEE 22nd International Symposium on the Physical and Failure Analysis of Integrated Circuits

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In semiconductor manufacturing for automotive, reliability constraints are strong: zero defect is expected by specific statistical techniques. In particular, distributions of the parametric electrical tests implemented at the probe or assembly steps, are controlled, test by test. In order to increase reliability in field by enhancement of potential failure screening, new techniques of test result distribution monitoring are studied with a multivariate approach, when all the test results are addressed simultaneously.

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Section: Test Selection By Outlying Rankingmentioning

confidence: 99%

Study and implementation conditions of the multivariate outlier detection methods for screening of potential field failures

Bergès¹,

Wu²,

Soufflet³

2015

2015 IEEE 22nd International Symposium on the Physical and Failure Analysis of Integrated Circuits

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“…It was shown that multivariate outlier analysis was more robust and effective at screening customer returns than traditional test limits. In [8], it was found that test selection is a critical step in the learning for screening customer returns. A methodology was suggested that leveraged various test selection algorithms to improve the ability to screen returns.…”

Section: Prior Related Workmentioning

confidence: 99%

“…outlier models. To apply multivariate test analysis, one challenge is to select the relevant combinations of tests to use [8]. This is necessary when there are many parametric tests, potentially over 1000, and the possible test combinations can be enormous.…”

Section: Introductionmentioning

confidence: 99%

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Screening customer returns with multivariate test analysis

Sumikawa

Tikkanen

Wang

et al. 2012

2012 IEEE International Test Conference

Self Cite

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This work studies the potential of capturing customer returns with models constructed based on multivariate analysis of parametric wafer sort test measurements. In such an analysis, subsets of tests are selected to build models for making pass/fail decisions. Two approaches are considered. A preemptive approach selects correlated tests to construct multivariate test models to screen out outliers. This approach does not rely on known customer returns. In contrast, a reactive approach selects tests relevant to a given customer return and builds an outlier model specific to the return. This model is applied to capture future parts similar to the return. The study is based on test data collected over roughly 16 months of production for a high-quality SoC sold to the automotive market. The data consists of 62 customer returns belonging to 52 lots. The study shows that each approach can capture returns not captured by the other. With both approaches, the study shows that multivariate test analysis can have a significant impact on reducing customer return rates especially during the later period of the production.

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“…For predicting customer returns, the works in [7], [8] apply test selection when learning from an extremely imbalanced dataset, where the imbalance was on the order of 10k vs. 1, i.e. 10k passing parts versus 1 failing part.…”

Section: Related Workmentioning

confidence: 99%

Forward prediction based on wafer sort data — A case study

Sumikawa

Drmanac

Wang

et al. 2011

2011 IEEE International Test Conference

Self Cite

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This paper studies the potential of using wafer probe tests to predict the outcome of future tests. The study is carried out using test data based on an SoC design for the automotive market. Given a set of known failing parts, there are two possible approaches to learn. First a single binary classification model can be learned to model all failing parts. We show that this approach can be effective if the failing parts are compatible in learning. Second, an individual outlier model can be learned for each failing part. We show that this approach is suitable for learning failing parts such as customer returns, where each may have a unique failing behavior. We also show that with Principal Component Analysis (PCA), a learning model can be visualized in two or three dimensional PC space, which facilitates an engineer to manually select or adjust the model.

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Important test selection for screening potential customer returns

Cited by 14 publications

References 6 publications

Study and implementation conditions of the multivariate outlier detection methods for screening of potential field failures

Study and implementation conditions of the multivariate outlier detection methods for screening of potential field failures

Screening customer returns with multivariate test analysis

Forward prediction based on wafer sort data — A case study

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Important test selection for screening potential customer returns

Cited by 14 publications

References 6 publications

Study and implementation conditions of the multivariate outlier detection methods for screening of potential field failures

Study and implementation conditions of the multivariate outlier detection methods for screening of potential field failures

Screening customer returns with multivariate test analysis

Forward prediction based on wafer sort data &#x2014; A case study

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Forward prediction based on wafer sort data — A case study