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: 著書/レポートタイプへの貢献会議での発言

抜粋

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.

元の言語英語
ホスト出版物のタイトル2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538662687
DOI
出版物ステータス出版済み - 2 22 2019
イベント2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Tokyo, 日本
継続期間: 12 10 201812 11 2018

出版物シリーズ

名前IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings
2018-December
ISSN(印刷物)1523-553X

会議

会議2018 International Symposium on Semiconductor Manufacturing, ISSM 2018
日本
Tokyo
期間12/10/1812/11/18

    フィンガープリント

All Science Journal Classification (ASJC) codes

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

これを引用

Okazaki, T., Okusa, K., & Yoshida, K. (2019). Prediction of the number of defects in image sensors by vm using equipment QC data. : 2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings [8651135] (IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings; 巻数 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSM.2018.8651135