IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-13074-8_6.html
   My bibliography  Save this book chapter

Machine Learning for Testability Prediction

In: Machine Learning Applications in Electronic Design Automation

Author

Listed:
  • Yuzhe Ma

    (The Hong Kong University of Science and Technology (Guangzhou))

Abstract

VLSI testing has become a significant concern in the modern design flow, which accounts for the reliability and development cost of a modern chip design. Recent advances in machine learning provide new methodologies to enhance various design stages in the design cycle. This chapter will discuss typical machine learning approaches for testability measurements, which focuses on a set of testability-related prediction problems in both component level and circuit level. In addition, several considerations on applying machine learning models for practical testability improvement are introduced.

Suggested Citation

  • Yuzhe Ma, 2022. "Machine Learning for Testability Prediction," Springer Books, in: Haoxing Ren & Jiang Hu (ed.), Machine Learning Applications in Electronic Design Automation, chapter 0, pages 151-180, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-13074-8_6
    DOI: 10.1007/978-3-031-13074-8_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-031-13074-8_6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.