rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning Support for Fault Diagnosis of System-on-Chip
  name:
    name part: Girard, Patrick.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Blanton, Shawn.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Wang, Li-C.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2023
    edition: 1st ed. 2023.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XI, 316 pages 165 illustrations)
  abstract: 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. 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, et cetera.
  table of 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.
  note: edited by Patrick Girard, Shawn Blanton, Li-C. Wang.
  subject:
    topic: Electronic circuits
  subject:
    topic: Electronic circuit design
  subject:
    topic: Microprocessors
  subject:
    topic: Computer architecture
  subject:
    topic: Electronic Circuits and Systems
  subject:
    topic: Electronics Design and Verification
  subject:
    topic: Processor Architectures
  classification: TK7867-7867.5
  classification: 621.3815
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9978-3-031-19639-3
  identifier: 9783031196393
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 230313
    record change date: 20240805185633.8
    record identifier: in00004656126