Knowledge-Driven Board-Level Functional Fault Diagnosis

154 Pages·2017·8.809 MB·English

by  Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu (auth.)

Fangming Ye · Zhaobo Zhang
Krishnendu Chakrabarty · Xinli Gu
Knowledge-
Driven
Board-Level
Functional
Fault Diagnosis
Knowledge-Driven Board-Level Functional
Fault Diagnosis
Fangming Ye Zhaobo Zhang
(cid:129)
Krishnendu Chakrabarty Xinli Gu
(cid:129)
Knowledge-Driven
Board-Level Functional
Fault Diagnosis
123
FangmingYe Krishnendu Chakrabarty
HuaweiTechnologies Department ofElectrical andComputer
SantaClara, CA Engineering
USA Duke University
Durham, NC
Zhaobo Zhang USA
HuaweiTechnologies
SantaClara, CA Xinli Gu
USA HuaweiTechnologies
SantaClara, CA
USA
ISBN978-3-319-40209-3 ISBN978-3-319-40210-9 (eBook)
DOI 10.1007/978-3-319-40210-9
LibraryofCongressControlNumber:2016942023
©SpringerInternationalPublishingSwitzerland2017
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The publisher, the authors and the editors are safe to assume that the advice and information in this
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To my beloved family for their boundless
support.
Fangming Ye
To those days and nights at graduate school.
Zhaobo Zhang
Preface
The semiconductor industry continues to relentlessly advance silicon technology
scaling into the deep-submicron (DSM) era. High integration levels and structured
designmethodsenablecomplexsystemsthatcanbemanufacturedinhighvolume.
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.
Astate-of-the-artdiagnosissysteminvolvesseveralkeycomponents:(1)design
of functional test programs, (2) collection of functional-failure syndromes,
(3)buildingofthediagnosisengine,(4)isolationofrootcauses,and(5)evaluation
of the diagnosis engine. Advances in each of these components can pave the way
for a more effective diagnosis system, thus improving diagnosis accuracy and
reducing diagnosis time. Machine-learning techniques offer an unprecedented
opportunity to develop an automated and adaptive diagnosis system to increase
diagnosis accuracy and speed.
This book provides a comprehensive set of characterization, prediction, opti-
mization,evaluation,andevolutiontechniquesforadiagnosissystem.Readerswith
a background in electronics design or system engineering can use this book as a
referencetoderiveinsightfulknowledgefromdataanalysisandusethisknowledge
as guidance for designing reasoning-based diagnosis systems. Meanwhile, readers
withabackgroundinstatisticsordataanalyticscanusethisbookasacasestudyfor
adapting data mining and machine-learning techniques to electronic system design
and diagnosis.
Thisbookidentifiesthekeychallengesinreasoning-basedboard-leveldiagnosis
system design, and presents machine-learning-based solutions and corresponding
results that have emerged from cutting edge research in this domain. It broadly
exploresaseriesoftopicsrangingfromhigh-accuracyfaultisolation,adaptivefault
isolation, diagnosis system robustness design, system performance analysis and
evaluation, knowledge discovery, and knowledge transfer.
vii
viii Preface
This book first describes a diagnosis system based on support-vector machine
(SVM), multi-kernel SVM (MK-SVM), and incremental learning. The MK-SVM
method leverages a linear combination of single kernels to achieve accurate
root-cause isolation. The MK-SVMs thus generated also can be updated based on
incremental learning. Furthermore, a data-fusion technique, namely majority-
weighted voting, is used to leverage multiple learning techniques for diagnosis.
The diagnosis time is considerable for complex boards due to the large number
ofsyndromesthatmustbeusedtoensurediagnosticaccuracy.Syndromecollection
and analysis are major bottlenecks in state-of-the-art diagnosis procedures.
Therefore, this book describes an adaptive diagnosis method based on decision
trees (DT). The number of syndromes required for diagnosis can be significantly
reduced compared to the number of syndromes used for system training.
Furthermore,anincrementalversionofDTsisusedtofacilitateonlinelearning,so
astobridgetheknowledgeobtainedattest-designstagewiththeknowledgegained
during volume production.
This book also includes an evaluation and enhancement framework based on
information theory for guiding diagnosis systems using syndrome and root-cause
analysis.Syndromeanalysisbasedonsubsetselectionprovidesarepresentativeset
of syndromes. Root-cause analysis measures the discriminative ability of differ-
entiating a given root cause from others. The metrics obtained from the proposed
framework can provide guidelines for test redesign to enhance diagnosis. In addi-
tion, traditional diagnosis systems fail to provide appropriate repair suggestions
when the diagnostic logs are fragmented and some syndromes are not available.
The feature of handling missing syndromes based on imputation methods has
therefore been added to the diagnosis system.
Finally, to tackle the bottleneck of data acquisition during the initial product
ramp-up phase, a knowledge-discovery method and a knowledge-transfer method
are proposed for enriching the training data set, thus facilitating board-level func-
tional fault diagnosis.
In summary, this book targets the realization of an automated diagnosis system
that offers the benefits of high accuracy, low diagnosis time, self-evaluation,
self-learning, and ability of selective learning from other diagnosis systems.
Although the goal of this work was to advance board-level diagnosis, the core
techniques developed in this book can also be leveraged for electronic systems
beyond the board level.
Santa Clara Fangming Ye
Santa Clara Zhaobo Zhang
Durham Krishnendu Chakrabarty
Santa Clara Xinli Gu
Acknowledgments
The authors acknowledge the support received from Huawei Technologies. The
authorsalsoappreciatethecontributionofShiJintotheworkonhandlingmissing
syndromes, described in Chap. 6.
ix
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Introduction to Manufacturing Test . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 System and Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Testing in the Manufacturing Line . . . . . . . . . . . . . . . . 3
1.2 Introduction to Board-Level Diagnosis . . . . . . . . . . . . . . . . . . 6
1.2.1 Review of State-of-the-Art . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Automation in Diagnosis System . . . . . . . . . . . . . . . . . 10
1.2.3 New Directions Enabled by Machine Learning . . . . . . . . 13
1.2.4 Challenges and Opportunities . . . . . . . . . . . . . . . . . . . . 15
1.3 Outline of Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2 Diagnosis Using Support Vector Machines (SVM) . . . . . . . . . . . . 23
2.1 Background and Chapter Highlights . . . . . . . . . . . . . . . . . . . . 24
2.2 Diagnosis Using Support Vector Machines . . . . . . . . . . . . . . . 25
2.2.1 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . 25
2.2.2 SVM Diagnosis Flow . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3 Multi-kernel Support Vector Machines and Incremental
Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3.1 Multi-kernel Support Vector Machines . . . . . . . . . . . . . 29
2.3.2 Incremental Learning . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.1 Evaluation of MK-SVM-Based Diagnosis System . . . . . 36
2.4.2 Evaluation of Incremental SVM-Based Diagnosis
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4.3 Evaluation of Incremental MK-SVM-Based
Diagnosis System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
xi
xii Contents
3 Diagnosis Using Multiple Classifiers and Majority-Weighted
Voting (WMV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 Background and Chapter Highlights . . . . . . . . . . . . . . . . . . . . 44
3.2 Artificial Neural Networks (ANN) . . . . . . . . . . . . . . . . . . . . . 45
3.2.1 Architecture of ANNs . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2.2 Demonstration of ANN-Based Diagnosis System . . . . . . 48
3.3 Comparison Between ANNs and SVMs . . . . . . . . . . . . . . . . . 49
3.4 Diagnosis Using Weighted-Majority Voting . . . . . . . . . . . . . . . 49
3.4.1 Weighted-Majority Voting . . . . . . . . . . . . . . . . . . . . . . 49
3.4.2 Demonstration of WMV-Based Diagnosis System . . . . . 51
3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.5.1 Evaluation of ANNs-Based Diagnosis System . . . . . . . . 52
3.5.2 Evaluation of SVMs-Based Diagnosis System . . . . . . . . 55
3.5.3 Evaluation of WMV-Based Diagnosis System . . . . . . . . 56
3.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4 Adaptive Diagnosis Using Decision Trees (DT) . . . . . . . . . . . . . . . 61
4.1 Background and Chapter Highlights . . . . . . . . . . . . . . . . . . . . 62
4.2 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.1 Training of Decision Trees . . . . . . . . . . . . . . . . . . . . . 63
4.2.2 Example of DT-Based Training and Diagnosis . . . . . . . . 65
4.3 Diagnosis Using Incremental Decision Trees . . . . . . . . . . . . . . 67
4.3.1 Incremental Tree Node . . . . . . . . . . . . . . . . . . . . . . . . 67
4.3.2 Addition of a Case . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3.3 Ensuring the Best Splitting . . . . . . . . . . . . . . . . . . . . . 70
4.3.4 Tree Transposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.4 Diagnosis Flow Based on Incremental Decision Trees . . . . . . . . 72
4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.5.1 Evaluation of DT-Based Diagnosis System . . . . . . . . . . 75
4.5.2 Evaluation of Incremental DT-Based
Diagnosis System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5 Information-Theoretic Syndrome and Root-Cause Evaluation . . . . 79
5.1 Background and Chapter Highlights . . . . . . . . . . . . . . . . . . . . 80
5.2 Evaluation Methods for Diagnosis Systems . . . . . . . . . . . . . . . 82
5.2.1 Subset Selection for Syndromes Analysis . . . . . . . . . . . 82
5.2.2 Class-Relevance Statistics . . . . . . . . . . . . . . . . . . . . . . 84
5.3 Evaluation and Enhancement Framework . . . . . . . . . . . . . . . . . 85
5.3.1 Evaluation and Enhancement Procedure . . . . . . . . . . . . 85
5.3.2 An Example of the Proposed Framework . . . . . . . . . . . 86