Board-Level Functional Fault Diagnosis Using Multikernel Support Vector Machines and Incremental Learning

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

doi:10.1109/tcad.2013.2287184

Cited by 57 publications

(39 citation statements)

References 25 publications

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“…The K-nearest neighbor (KNN) and sample mean imputation (SMI) have also been used to carry out the imputation of missing data [21]. A reasoning-based diagnosis flow in [12] can be improved by integrating the component of preprocessing missing values, as shown in Fig. 3.…”

Section: Methods To Handle Missing Syndromesmentioning

confidence: 99%

“…Recent work on board-level fault diagnosis has shown that machine-learning techniques can be adopted to automate the process of identifying faulty candidates (components) based on the historical data of successfully repaired boards [11], [12]. A flowchart of such a diagnosis system is shown in Fig.…”

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

“…In prior work, e.g., in [12], functional-test logs were parsed in a coarse manner where a syndrome is 1 (fail) if a functional test leads to an error at the corresponding observation point. All other functional tests and observation-point pairs, which are mapped to syndromes, are deemed as 0 (pass); see Fig. 2(b) [12], [13], [15].…”

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

“…All other functional tests and observation-point pairs, which are mapped to syndromes, are deemed as 0 (pass); see Fig. 2(b) [12], [13], [15]. However, a syndrome should not be denoted as either fail or pass when the observation of the test is not available in practice.…”

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

“…Machine-learning techniques facilitate reasoning-based diagnosis, providing the benefits of ease of implementation, high diagnosis accuracy, and continuous learning. The repair/replacement of faulty components is suggested through a ranked list of suspect components, e.g., based on artificial neural networks (ANNs), support-vector machines (SVMs), and decision trees (DTs) [12], [13]. However, the diagnosis accuracy of these methods may be significantly reduced when the repair logs are fragmented and some errors, or syndromes, are not available during diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

Jin

Zhang

et al. 2013

2013 22nd Asian Test Symposium

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Functional fault diagnosis is widely used in board manufacturing to ensure product quality and improve product yield. Advanced machine-learning techniques have recently been advocated for reasoning-based diagnosis; these technologies are based on historical data of successfully repaired boards. However, traditional diagnosis systems fail to provide appropriate repair suggestions when the diagnostic logs are fragmented and some error outcomes, or syndromes, are not available during diagnosis. We describe the design of a diagnosis system, based on supportvector machines, that can handle missing syndromes by using the method of imputation. Several imputation methods are discussed and compared in terms of their efficiency in handling missing syndromes. Two large-scale synthetic data sets generated from the log information of complex industrial boards in volume production are used to validate the proposed diagnosis system in terms of diagnosis accuracy and training time.

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Section: Methods To Handle Missing Syndromesmentioning

confidence: 99%

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

Jin

Zhang

et al. 2013

2013 22nd Asian Test Symposium

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A survey of digital circuit testing in the light of machine learning

Pradhan

Bhattacharya

2020

WIREs Data Min & Knowl

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The insistent trend in today's nanoscale technology, to keep abreast of the Moore's law, has been continually opening up newer challenges to circuit designers. With rapid downscaling of integration, the intricacies involved in the manufacturing process have escalated significantly. Concomitantly, the nature of defects in silicon chips has become more complex and unpredictable, adding further difficulty in circuit testing and diagnosis. The volume of test data has surged and the parameters that govern testing of integrated circuits have increased not only in dimension but also in the complexity of their correlation. Evidently, the current scenario serves as a pertinent platform to explore new test solutions based on machine learning. In this survey, we look at various recent advances in this evolving domain in the context of digital logic testing and diagnosis. This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Machine Learning Technologies > Prediction

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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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Board-Level Functional Fault Diagnosis Using Multikernel Support Vector Machines and Incremental Learning

Cited by 57 publications

References 25 publications

Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

A survey of digital circuit testing in the light of machine learning

Defect Diagnosis Techniques for Silicon Customer Returns

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