Название: Machine Learning Support for Fault Diagnosis of System-on-ChipАвтор: Patrick Girard, Shawn Blanton, Li-C. Wang
Издательство: Springer
Год: 2023
Страниц: 320
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB
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. Today’s electronic systems are composed of complex Systems on a Chip (SoCs) made of heterogeneous blocks that comprise memories, digital circuits, analog and mixed-signal circuits, etc. To fit a critical application standard requirement, SoCs pass through a comprehensive test flow (functional, structural, parametric, etc.) at the end of the manufacturing process.
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