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Machine learning support for fault d...

Girard, Patrick.

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Machine learning support for fault diagnosis of System-on-Chip

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning support for fault diagnosis of System-on-Chip/ edited by Patrick Girard, Shawn Blanton, Li-C. Wang.
other author:	Girard, Patrick.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xi, 316 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Prerequisites on Fault Diagnosis -- Conventional Methods for Fault Diagnosis -- Machine Learning and Its Applications in Test -- Machine Learning Support for Logic Diagnosis -- Machine Learning Support for Cell-Aware Diagnosis -- Machine Learning Support for Volume Diagnosis -- Machine Learning Support for Diagnosis of Analog Circuits -- Machine Learning Support for Board-level Functional Fault Diagnosis -- Machine Learning Support for Wafer-level Failure Cluster Identification -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Electric fault location. -
Online resource:	https://doi.org/10.1007/978-3-031-19639-3
ISBN:	9783031196393

Machine learning support for fault diagnosis of System-on-Chip
Machine learning support for fault diagnosis of System-on-Chip[electronic resource] /edited by Patrick Girard, Shawn Blanton, Li-C. Wang. - Cham :Springer International Publishing :2023. - xi, 316 p. :ill., digital ;24 cm.

Introduction -- Prerequisites on Fault Diagnosis -- Conventional Methods for Fault Diagnosis -- Machine Learning and Its Applications in Test -- Machine Learning Support for Logic Diagnosis -- Machine Learning Support for Cell-Aware Diagnosis -- Machine Learning Support for Volume Diagnosis -- Machine Learning Support for Diagnosis of Analog Circuits -- Machine Learning Support for Board-level Functional Fault Diagnosis -- Machine Learning Support for Wafer-level Failure Cluster Identification -- Conclusion.

This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques. The benefits of the book for the reader are: Identifies the key challenges in fault diagnosis of system-on-chip and presents the solutions and corresponding results that have emerged from leading-edge research; Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing; Includes necessary background information on testing and diagnosis and a compendium of solutions existing in this field; Demonstrates techniques based on industrial data and feedback from actual PFA analysis; Discusses practical problems, including test sequence quality, diagnosis resolution, accuracy, time cost, etc.

ISBN: 9783031196393

Standard No.: 10.1007/978-3-031-19639-3doiSubjects--Topical Terms:

836008
Electric fault location.

LC Class. No.: TK3226 / .M33 2023

Dewey Class. No.: 621.3815

Machine learning support for fault diagnosis of System-on-Chip
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245 0 0 $a Machine learning support for fault diagnosis of System-on-Chip $h [electronic resource] / $c edited by Patrick Girard, Shawn Blanton, Li-C. Wang.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xi, 316 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Prerequisites on Fault Diagnosis -- Conventional Methods for Fault Diagnosis -- Machine Learning and Its Applications in Test -- Machine Learning Support for Logic Diagnosis -- Machine Learning Support for Cell-Aware Diagnosis -- Machine Learning Support for Volume Diagnosis -- Machine Learning Support for Diagnosis of Analog Circuits -- Machine Learning Support for Board-level Functional Fault Diagnosis -- Machine Learning Support for Wafer-level Failure Cluster Identification -- Conclusion.
520 $a This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques. The benefits of the book for the reader are: Identifies the key challenges in fault diagnosis of system-on-chip and presents the solutions and corresponding results that have emerged from leading-edge research; Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing; Includes necessary background information on testing and diagnosis and a compendium of solutions existing in this field; Demonstrates techniques based on industrial data and feedback from actual PFA analysis; Discusses practical problems, including test sequence quality, diagnosis resolution, accuracy, time cost, etc.
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650 0 $a Machine learning. $3 533906
650 1 4 $a Electronic Circuits and Systems. $3 3538814
650 2 4 $a Electronics Design and Verification. $3 3592716
650 2 4 $a Processor Architectures. $3 892680
700 1 $a Girard, Patrick. $3 1086104
700 1 $a Blanton, Shawn. $3 3631536
700 1 $a Wang, Li-C. $3 3631537
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856 4 0 $u https://doi.org/10.1007/978-3-031-19639-3
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