Prediction of the number of defects in image sensors by vm using equipment QC data

Toshiya Okazaki, Kosuke Okusa, Kyo Yoshida

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    This paper describes methods and evaluation results of predicting the number of defects in image sensors. We used regression tree and stepwise AIC for variable selection and generalized linear model for regression, instead of partial least squares (PLS) regression. The results showed improvement in prediction performance in comparison with the conventional method. By this, we could predict other countable values such as defects or dust particles.

    Original languageEnglish
    Title of host publication2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781538662687
    DOIs
    Publication statusPublished - Jul 2 2018
    Event2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Tokyo, Japan
    Duration: Dec 10 2018Dec 11 2018

    Publication series

    NameIEEE International Symposium on Semiconductor Manufacturing Conference Proceedings
    Volume2018-December
    ISSN (Print)1523-553X

    Conference

    Conference2018 International Symposium on Semiconductor Manufacturing, ISSM 2018
    Country/TerritoryJapan
    CityTokyo
    Period12/10/1812/11/18

    All Science Journal Classification (ASJC) codes

    • Electronic, Optical and Magnetic Materials
    • General Engineering
    • Industrial and Manufacturing Engineering
    • Electrical and Electronic Engineering

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