Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees

Ye, Fangming; Zhang, Zhaobo; Chakrabarty, Krishnendu; Gu, Xinli

doi:10.1109/tcad.2015.2459046

Cited by 19 publications

(2 citation statements)

References 27 publications

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“…Existing effective system-level fault diagnosis techniques either apply only for boards [20] or, if they apply for SoCs, then they target the digital part of the SoC [21]. In particular, the individual blocks can be accessed and tested in isolation so as to identify the faulty block.…”

Section: System-level Fault Diagnosismentioning

confidence: 99%

Defect Diagnosis Techniques for Silicon Customer Returns

Girard¹,

Bosio²,

Ladhar³

et al. 2023

Frontiers of Quality Electronic Design (QED)

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This chapter provides an overview of the various approaches and techniques proposed so far for defect diagnosis in silicon customer returns. It focuses on diagnosis of defects in logic blocks of SoCs. After some backgrounds on test and fault diagnosis, the chapter presents the various test scenarios used in practice during customer return diagnosis. A discussion on the quality required by the test sequences used during customer return is also proposed. Then, the chapter reviews the stateof-the-art techniques existing to identify defects at the cell level (called intra-cell or cell-aware diagnosis). A summary of conventional approaches is first proposed. Then, the latest Machine Learning (ML) -based cell-aware diagnosis techniques are reviewed. Effectiveness of existing ML techniques is shown through industrial case studies and corresponding diagnosis results in terms of accuracy and resolution. The chapter ends with a discussion on the future directions in this field.

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Section: System-level Fault Diagnosismentioning

confidence: 99%

Defect Diagnosis Techniques for Silicon Customer Returns

Girard¹,

Bosio²,

Ladhar³

et al. 2023

Frontiers of Quality Electronic Design (QED)

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“…Ye et al. 12 proposed an adaptive diagnosis method based on incremental decision trees for board-level functional fault diagnosis. Faulty components are classified according to the discriminative ability of the syndromes in decision tree training.…”

mentioning

confidence: 99%

Integration of multivariate control charts and the decision tree classifier to determine the faults of the quality characteristic(s) of a melt spinning machine used in polypropylene fiber manufacturing. Part II: The application of multivariate control charts and the decision tree classifier to determine the faults of quality characteristic(s)

Kuo

Huang

Yang

et al. 2021

Textile Research Journal

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In this study, a multivariate statistical process control was used to analyze the abnormal samples derived from the deviation of optimum processing parameters. The experimental samples derived from the optimum processing parameters were applied as the optimal historical data to determine the control limit, and then the T2 value was obtained from Hotelling's T2 method. If the T2 value exceeds the control limit, the corresponding sample is considered as abnormal. After that, the Runger, Alt and Montgomery method is used to decompose the abnormal T2 value. Then, each quality characteristic value can be obtained and the corresponding decision tree classifier can be implemented. To improve the classification accuracy, we classify the decision tree classifier into single–double identification, single-factor abnormality and double-factor abnormality. For the individual classification test, the result showed that the accuracy of single–double identification was 98.6%, the single-factor abnormality classification was 100% and the double-factor abnormality classification was 96.0%. For the combination classification test, we can get a 98.6% accuracy rate for the single–double identification, 98.3% accuracy rate for the single-factor abnormality classification and 95.3% accuracy rate for the double-factor abnormality classification. Therefore, it can be confirmed that the proposed methods in this study can effectively identify abnormal samples and establish a fault processing parameter diagnosis system for melt spinning machines.

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Machine Learning: Fundamentals

Kang

Jameson

2018

Prognostics and Health Management of Electronics

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Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees

Cited by 19 publications

References 27 publications

Defect Diagnosis Techniques for Silicon Customer Returns

Defect Diagnosis Techniques for Silicon Customer Returns

Machine Learning: Fundamentals

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