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

Toshiya Okazaki, Kosuke Okusa, Kyo Yoshida

研究成果: Contribution to journalArticle

抜粋

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.

元の言語英語
記事番号8839510
ページ(範囲)434-437
ページ数4
ジャーナルIEEE Transactions on Semiconductor Manufacturing
32
発行部数4
DOI
出版物ステータス出版済み - 11 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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