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.
General
Imprint: |
Springer International Publishing AG
|
Country of origin: |
Switzerland |
Release date: |
March 2023 |
First published: |
2023 |
Editors: |
Patrick Girard
• Shawn Blanton
• Li-C. Wang
|
Dimensions: |
235 x 155mm (L x W) |
Format: |
Hardcover
|
Pages: |
316 |
Edition: |
1st ed. 2023 |
ISBN-13: |
978-3-03-119638-6 |
Categories: |
Books
|
LSN: |
3-03-119638-4 |
Barcode: |
9783031196386 |
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