IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v48y2016i1p1-15.html
   My bibliography  Save this article

Monitoring wafers’ geometric quality using an additive Gaussian process model

Author

Listed:
  • Linmiao Zhang
  • Kaibo Wang
  • Nan Chen

Abstract

The geometric quality of a wafer is an important quality characteristic in the semiconductor industry. However, it is difficult to monitor this characteristic during the manufacturing process due to the challenges created by the complexity of the data structure. In this article, we propose an Additive Gaussian Process (AGP) model to approximate a standard geometric profile of a wafer while quantifying the deviations from the standard when a manufacturing process is in an in-control state. Based on the AGP model, two statistical tests are developed to determine whether or not a newly produced wafer is conforming. We have conducted extensive numerical simulations and real case studies, the results of which indicate that our proposed method is effective and has potentially wide application.

Suggested Citation

  • Linmiao Zhang & Kaibo Wang & Nan Chen, 2016. "Monitoring wafers’ geometric quality using an additive Gaussian process model," IISE Transactions, Taylor & Francis Journals, vol. 48(1), pages 1-15, January.
  • Handle: RePEc:taf:uiiexx:v:48:y:2016:i:1:p:1-15
    DOI: 10.1080/0740817X.2015.1027455
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0740817X.2015.1027455
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/0740817X.2015.1027455?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Spatial randomness-based anomaly detection approach for monitoring local variations in multimode surface topography," Annals of Operations Research, Springer, vol. 341(1), pages 173-195, October.
    2. Chen Zhao & Shichang Du & Jun Lv & Yafei Deng & Guilong Li, 2023. "A novel parallel classification network for classifying three-dimensional surface with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 515-527, February.

    More about this item

    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:taf:uiiexx:v:48:y:2016:i:1:p:1-15. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    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.