Prediction of the number of defects in image sensors by VM using equipment QC Data

Toshiya Okazaki, Kosuke Okusa, Kyo Yoshida

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    This paper describes methods and evaluation results of predicting the number of defects in image sensors using equipment QC data. Virtual metrology (VM) models are mainly used for measurable values such as dimensions and electrical characteristics. Herein, to predict countable values, we used a regression tree and stepwise AIC for variable selection as well as the 'hockey-stick regression model' and generalized linear model for regression, instead of the partial least squares (PLS) regression. The results showed an improved prediction performance in comparison with the conventional method. This method can be used to predict other countable values such as defects or dust particles.

    Original languageEnglish
    Article number8839510
    Pages (from-to)434-437
    Number of pages4
    JournalIEEE Transactions on Semiconductor Manufacturing
    Volume32
    Issue number4
    DOIs
    Publication statusPublished - Nov 2019

    All Science Journal Classification (ASJC) codes

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Industrial and Manufacturing Engineering
    • Electrical and Electronic Engineering

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