001461575 000__ 04668cam\a22006257i\4500
001461575 001__ 1461575
001461575 003__ OCoLC
001461575 005__ 20230503003359.0
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001461575 008__ 230325s2023\\\\sz\\\\\\ob\\\\001\0\eng\d
001461575 019__ $$a1373013649
001461575 020__ $$a9783031196393$$qelectronic book
001461575 020__ $$a3031196392$$qelectronic book
001461575 020__ $$z9783031196386
001461575 020__ $$z3031196384
001461575 0247_ $$a10.1007/978-3-031-19639-3$$2doi
001461575 035__ $$aSP(OCoLC)1373349029
001461575 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dUKAHL$$dYDX$$dOCLCF
001461575 049__ $$aISEA
001461575 050_4 $$aTK3226$$b.M33 2023
001461575 08204 $$a621.3815$$223/eng/20230327
001461575 24500 $$aMachine learning support for fault diagnosis of System-on-Chip /$$cPatrick Girard, Shawn Blanton, Li-C. Wang, editors.
001461575 264_1 $$aCham :$$bSpringer,$$c[2023]
001461575 300__ $$a1 online resource
001461575 336__ $$atext$$btxt$$2rdacontent
001461575 337__ $$acomputer$$bc$$2rdamedia
001461575 338__ $$aonline resource$$bcr$$2rdacarrier
001461575 504__ $$aIncludes bibliographical references and index.
001461575 5050_ $$aIntroduction -- 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.
001461575 506__ $$aAccess limited to authorized users.
001461575 520__ $$aThis 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. The benefits of the book for the reader are: Identifies the key challenges in fault diagnosis of system-on-chip and presents the solutions and corresponding results that have emerged from leading-edge research; Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing; Includes necessary background information on testing and diagnosis and a compendium of solutions existing in this field; Demonstrates techniques based on industrial data and feedback from actual PFA analysis; Discusses practical problems, including test sequence quality, diagnosis resolution, accuracy, time cost, etc.
001461575 588__ $$aDescription based on online resource; title from digital title page (viewed on April 18, 2023).
001461575 650_0 $$aElectric fault location.
001461575 650_0 $$aSystems on a chip.
001461575 650_0 $$aMachine learning.
001461575 655_0 $$aElectronic books.
001461575 7001_ $$aGirard, Patrick,$$cPh. D.,$$eeditor.
001461575 7001_ $$aBlanton, Shawn,$$eeditor.
001461575 7001_ $$aWang, Li-C.,$$d1963-$$eeditor.
001461575 77608 $$iPrint version:$$aGirard, Patrick$$tMachine Learning Support for Fault Diagnosis of System-On-Chip$$dCham : Springer International Publishing AG,c2023$$z9783031196386
001461575 852__ $$bebk
001461575 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-19639-3$$zOnline Access$$91397441.1
001461575 909CO $$ooai:library.usi.edu:1461575$$pGLOBAL_SET
001461575 980__ $$aBIB
001461575 980__ $$aEBOOK
001461575 982__ $$aEbook
001461575 983__ $$aOnline
001461575 994__ $$a92$$bISE