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査読

1 被引用数 (Scopus)

抄録

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.

本文言語英語
論文番号8839510
ページ(範囲)434-437
ページ数4
ジャーナルIEEE Transactions on Semiconductor Manufacturing
32
4
DOI
出版ステータス出版済み - 11 2019

All Science Journal Classification (ASJC) codes

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • 産業および生産工学
  • 電子工学および電気工学

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