Information-theoretic syndrome and root-cause analysis for guiding board-level fault diagnosis

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

doi:10.1109/ets.2013.6569364

Cited by 10 publications

(13 citation statements)

References 19 publications

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“…However, structural information is not always accessible to the diagnosis engine, which limits the applicability of the AgentDiag method. An evaluation framework has recently been proposed to target the evaluation problem from the perspective of information theory and statistics [8], [9]. The proposed evaluation framework consisted of syndrome analysis and root-cause analysis, but more analysis and a detailed study of this framework is needed to make it practical.…”

Section: Functional-fault Diagnosis Systemmentioning

confidence: 99%

“…This paper extends the evaluation framework based on [8], [9]. In syndrome analysis, the evaluation framework carries out rigorous syndrome analysis based on the new feature selection method, namely correlation-based feature selection (CFS).…”

Section: Functional-fault Diagnosis Systemmentioning

confidence: 99%

“…Several techniques for board-level functional fault diagnosis have been adopted in industry, among which rule-based, model-based, and reasoning-based techniques have been popular over the past decade [5]- [7]. However, all these methods are likely to suffer low diagnosis accuracy without appropriate detailed evaluation [8], [9]. A rigorous framework is needed to evaluate an existing diagnosis system using quantifiable metrics, and provide means for enhancing the accuracy of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

Zhang

Chakrabarty

et al. 2015

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Reasoning-based functional-fault diagnosis has recently been advocated to achieve high diagnosis accuracy, low defect escapes, and reducing manufacturing cost. However, such diagnosis method requires a rich set of test items (syndromes) and a sizable database of faulty boards to learn from. An insufficient number of failed boards, ambiguous root-cause identification, and redundant or irrelevant syndromes can render reasoningbased diagnosis ineffective. Periodic evaluation and analysis can help locate weaknesses in a diagnosis system and thereby provide guidelines for redesigning the tests, which facilitates better diagnosis. We propose an information-theoretic framework for evaluating the effectiveness of and providing guidance to a reasoning-based functional-fault diagnosis system. Syndrome analysis based on feature selection methods provides a representative set of syndromes and suggests irrelevant syndromes in diagnosis. Root-cause analysis measures the discriminative ability of differentiating a given root cause from others. Results are presented for four types of diagnosis systems for three complex boards that are in volume production.

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Section: Functional-fault Diagnosis Systemmentioning

confidence: 99%

Section: Functional-fault Diagnosis Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

Zhang

Chakrabarty

et al. 2015

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…An evaluation framework based on minimum-redundancy-maximum-relevance (mRMR) has been used to identify a set of syndromes with high discriminative ability [37]. However, it provides no diagnosis ability and requires additional algorithms for diagnosis.…”

Section: Reasoning-based Methodsmentioning

confidence: 99%

Knowledge-Driven Board-Level Functional Fault Diagnosis

Zhang

Chakrabarty

et al. 2017

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The semiconductor industry continues to relentlessly advance silicon technology scaling into the deep-submicron (DSM) era. High integration levels and structured design methods enable complex systems that can be manufactured in high volume.However, due to increasing integration densities and high operating speeds, subtle manifestation of defects leads to functional failures at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results can lead to long debug times and wrong repair actions, which significantly increase repair cost and adversely impact yield.

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“…Much research has been devoted to the extraction of an effective model of the system from a limited amount of historic data [10], [11]. In this work we do not address the problem of incomplete or insufficient information, focusing instead on the other alternative for building the decision tree, starting from a model provided by the test/diagnosis engineer, as the one used by commercial tools [12], [13] typically adopted in industrial environments.…”

Section: The Proposed Diagnosis Approachmentioning

confidence: 99%

A configurable board-level adaptive incremental diagnosis technique based on decision trees

Bolchini

Cassano

2015

2015 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS)

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Abstract-Functional diagnosis for complex electronic boards is a time-consuming task that requires big expertise to the diagnosis engineers. In this paper we propose a new engine for board-level adaptive incremental functional diagnosis based on decision trees. The engine incrementally selects the tests that have to be executed and based on the test outcomes it automatically stops the diagnosis as soon as one or more faulty candidates can be identified, thus allowing to reduce the number of executed tests. Moreover, we propose a configurable early stop condition for the engine that allows to further reduce the number of executed tests leveraging the diagnosis accuracy. The effectiveness of the proposed approach has been assessed using a set of synthetic but realistic boards and three industrial boards.

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Information-theoretic syndrome and root-cause analysis for guiding board-level fault diagnosis

Cited by 10 publications

References 19 publications

Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

Knowledge-Driven Board-Level Functional Fault Diagnosis

A configurable board-level adaptive incremental diagnosis technique based on decision trees

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