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

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 - Feb 22 2019
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
CountryJapan
CityTokyo
Period12/10/1812/11/18

Fingerprint

Image sensors
Defects
Particles (particulate matter)
Dust

All Science Journal Classification (ASJC) codes

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

Cite this

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

Prediction of the number of defects in image sensors by vm using equipment QC data. / Okazaki, Toshiya; Okusa, Kosuke; Yoshida, Kyo.

2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8651135 (IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings; Vol. 2018-December).

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

Okazaki, T, Okusa, K & Yoshida, K 2019, Prediction of the number of defects in image sensors by vm using equipment QC data. in 2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings., 8651135, IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., 2018 International Symposium on Semiconductor Manufacturing, ISSM 2018, Tokyo, Japan, 12/10/18. https://doi.org/10.1109/ISSM.2018.8651135
Okazaki T, Okusa K, Yoshida K. Prediction of the number of defects in image sensors by vm using equipment QC data. In 2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8651135. (IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings). https://doi.org/10.1109/ISSM.2018.8651135
Okazaki, Toshiya ; Okusa, Kosuke ; Yoshida, Kyo. / Prediction of the number of defects in image sensors by vm using equipment QC data. 2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings).
@inproceedings{ec5b25cfa7814f109b258bab4ec6e8ed,
title = "Prediction of the number of defects in image sensors by vm using equipment QC data",
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.",
author = "Toshiya Okazaki and Kosuke Okusa and Kyo Yoshida",
year = "2019",
month = "2",
day = "22",
doi = "10.1109/ISSM.2018.8651135",
language = "English",
series = "IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings",
address = "United States",

}

TY - GEN

T1 - Prediction of the number of defects in image sensors by vm using equipment QC data

AU - Okazaki, Toshiya

AU - Okusa, Kosuke

AU - Yoshida, Kyo

PY - 2019/2/22

Y1 - 2019/2/22

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85063230865&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063230865&partnerID=8YFLogxK

U2 - 10.1109/ISSM.2018.8651135

DO - 10.1109/ISSM.2018.8651135

M3 - Conference contribution

AN - SCOPUS:85063230865

T3 - IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings

BT - 2018 International Symposium on Semiconductor Manufacturing, ISSM 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -