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Verlag: Springer International Publishing, Springer Nature Switzerland Mär 2024, 2024
ISBN 10: 3031196414 ISBN 13: 9783031196416
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.
Verlag: Springer International Publishing Mär 2024, 2024
ISBN 10: 3031196414 ISBN 13: 9783031196416
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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Verlag: Springer International Publishing, Springer Nature Switzerland Mär 2023, 2023
ISBN 10: 3031196384 ISBN 13: 9783031196386
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.
Verlag: Springer International Publishing, Springer Nature Switzerland, 2023
ISBN 10: 3031196384 ISBN 13: 9783031196386
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Verlag: Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031196414 ISBN 13: 9783031196416
Sprache: Englisch
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Verlag: Springer, Berlin, Springer International Publishing, Springer, 2024
ISBN 10: 3031196414 ISBN 13: 9783031196416
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In den WarenkorbTaschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 316 pp. Englisch.
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031196384 ISBN 13: 9783031196386
Sprache: Englisch
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In den WarenkorbGebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 m.
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In den WarenkorbZustand: New. PRINT ON DEMAND.
Verlag: Springer International Publishing, Springer International Publishing Mär 2023, 2023
ISBN 10: 3031196384 ISBN 13: 9783031196386
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 328 pp. Englisch.
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