Multidimensional parametric test set optimization of wafer probe data for predicting in field failures and setting tighter test limits

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

doi:10.1109/date.2011.5763135

Cited by 19 publications

(4 citation statements)

References 7 publications

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“…The work in [9,11] analyzed a set of parametric wafer probe test measurements in order to understand what it takes to screen out customer returns using outlier analysis. It was shown that multivariate outlier analysis was more robust and effective at screening customer returns than traditional test limits.…”

Section: Prior Related Workmentioning

confidence: 99%

Screening customer returns with multivariate test analysis

Sumikawa

Tikkanen

Wang

et al. 2012

2012 IEEE International Test Conference

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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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Section: Prior Related Workmentioning

confidence: 99%

Screening customer returns with multivariate test analysis

Sumikawa

Tikkanen

Wang

et al. 2012

2012 IEEE International Test Conference

View full text Add to dashboard Cite

show abstract

“…In another example, the authors in [11] analyzed parametric wafer sort data from a high quality SoC and showed the potential for building models from the test data which were capable of predicting devices likely-to-fail at final package testing. Similarly, multivariate test analysis was used in [10,12] to predict parts that would fail in the field, i.e. customer returns.…”

Section: Related Workmentioning

confidence: 99%

“…Test selection algorithms were applied in previous work to identify the tests which are most important in describing the failing signature for customer returns [10,11,12]. In this work, we apply the same SVM test ranking algorithm, which applies the C-Support Vector Classification (C-SVC) algorithm [1] to determine the importance of each test.…”

Section: Test Selection For Multivariate Test Analysismentioning

confidence: 99%

An experiment of burn-in time reduction based on parametric test analysis

Sumikawa

Wang

Abadir

2012

2012 IEEE International Test Conference

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Burn-in is a common test approach to screen out unreliable parts. The cost of burn-in can be significant due to long burn-in periods and expensive equipment. This work studies the potential of using parametric test data to reduce the time of burn-in. The experiment focuses on developing parametric test models based on test data collected after 10 hours of burn-in to predict parts likely-to-fail after 24 and 48 hours of burn-in. Our study shows that 24-hour and 48-hour burn-in failures behave abnormally in multivariate parametric test spaces after 10 hours of burn-in. Hence, it is possible to develop multivariate test models to identify these likely-to-fail parts early in a burn-in cycle. This study is carried out on 8 lots of test data from a burn-in experiment based on a 3-axis accelerometer design. The study shows that after 10 hours of burn-in, it is possible to identify a large portion of all parts that do not require longer burn-in time, potentially providing significant cost saving.

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“…One way to tackle these problems is to develop new multivariate outlier detection methods that cover all test parameters at once. For example, [3] proposes a multivariate kernel density approach and [4] describes a support vector machine approach. Because estimation errors grow rapidly with increasing dimension, these methods also must select the most important tests on which outlier detection is based.…”

Section: Introductionmentioning

confidence: 99%

Selection of tests for outlier detection

Bossers

Hurink

Smit

2013

2013 IEEE 31st VLSI Test Symposium (VTS)

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Integrated circuits are tested thoroughly in order to meet the high demands on quality. As an additional step, outlier detection is used to detect potential unreliable chips such that quality can be improved further. However, it is often unclear to which tests outlier detection should be applied and how the parameters must be set, such that outliers are detected and yield loss remains limited. In this paper we introduce a mathematical framework, that given a set of target devices, can select tests for outlier detection and set the parameters for each outlier detection method. We provide results on real world data and analyze the resulting yield loss and missed targets. 2013 IEEE 31st VLSI Test Symposium (VTS)

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Multidimensional parametric test set optimization of wafer probe data for predicting in field failures and setting tighter test limits

Cited by 19 publications

References 7 publications

Screening customer returns with multivariate test analysis

Screening customer returns with multivariate test analysis

An experiment of burn-in time reduction based on parametric test analysis

Selection of tests for outlier detection

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