Machine learning support for fault diagnosis of System-on-Chip / Patrick Girard, Shawn Blanton, Li-C. Wang, editors.
2023
TK3226 .M33 2023
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Unlimited
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Can lend chapters, not whole ebooks
Details
Title
Machine learning support for fault diagnosis of System-on-Chip / Patrick Girard, Shawn Blanton, Li-C. Wang, editors.
ISBN
9783031196393 electronic book
3031196392 electronic book
9783031196386
3031196384
3031196392 electronic book
9783031196386
3031196384
Published
Cham : Springer, [2023]
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-3-031-19639-3 doi
Call Number
TK3226 .M33 2023
Dewey Decimal Classification
621.3815
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on April 18, 2023).
Available in Other Form
Machine Learning Support for Fault Diagnosis of System-On-Chip
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Online Access
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Table of Contents
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.
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.