Machine Learning Support for Fault Diagnosis of System-on-Chip

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.

Format: Hardback
Length: 316 pages
Publication date: 14 March 2023
Publisher: Springer International Publishing AG

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.

Weight: 658g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031196386
Edition number: 1st ed. 2023