LEADER 03895nam 2200529 450 001 9910682590803321 005 20230529011239.0 010 $a3-031-19639-2 024 7 $a10.1007/978-3-031-19639-3 035 $a(MiAaPQ)EBC7214600 035 $a(Au-PeEL)EBL7214600 035 $a(CKB)26271278300041 035 $a(DE-He213)978-3-031-19639-3 035 $a(PPN)26909850X 035 $a(EXLCZ)9926271278300041 100 $a20230529d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning support for fault diagnosis of system-on-chip /$fedited by Patrick Girard, Shawn Blanton, and Li-C Wang 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (320 pages) 311 08$aPrint version: Girard, Patrick Machine Learning Support for Fault Diagnosis of System-On-Chip Cham : Springer International Publishing AG,c2023 9783031196386 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aElectric fault location 606 $aMachine learning 606 $aSystems on a chip 615 0$aElectric fault location. 615 0$aMachine learning. 615 0$aSystems on a chip. 676 $a006.31 702 $aGirard$b Patrick 702 $aBlanton$b Shawn 702 $aWang$b Li-C 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910682590803321 996 $aMachine learning support for fault diagnosis of system-on-chip$93389023 997 $aUNINA