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Full Description
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.
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.
