Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process

Kyuchang Chang; Jun Geol Baek

doi:10.1109/CSE/EUC.2019.00051

Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process

Kyuchang Chang
, Jun Geol Baek^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Many studies using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. This is because sensor signals generated in the semiconductor production process provide important information for predicting quality and yield of the finished product. However, as the process becomes more sophisticated and refined, normal and abnormal data with similar shape appears. They only show delicate differences and it is difficult to classify them using general classification algorithms. The purpose of this research is to present a preprocessing methodology for improving classification performance. The methodology consists of four steps based on signal segmentation and clustering methods. The experimental results illustrate the better performance of the proposed procedure.

Original language	English
Title of host publication	Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
Editors	Meikang Qiu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	230-232
Number of pages	3
ISBN (Electronic)	9781728116631
DOIs	https://doi.org/10.1109/CSE/EUC.2019.00051
Publication status	Published - 2019 Aug
Event	22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 - New York, United States Duration: 2019 Aug 1 → 2019 Aug 3

Publication series

Name	Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019

Conference

Conference	22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
Country/Territory	United States
City	New York
Period	19/8/1 → 19/8/3

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C2005949)

Publisher Copyright:
© 2019 IEEE.

Keywords

Time Series Classification(TSC) Feature Extraction Fault Detection and Classification(FDC) Clustering Hierarchical Clustering Segmentation

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture
Human-Computer Interaction
Artificial Intelligence

Access to Document

10.1109/CSE/EUC.2019.00051

Cite this

Chang, K., & Baek, J. G. (2019). Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process. In M. Qiu (Ed.), Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 (pp. 230-232). Article 8919603 (Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSE/EUC.2019.00051

Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process. / Chang, Kyuchang; Baek, Jun Geol.
Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019. ed. / Meikang Qiu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 230-232 8919603 (Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chang, K & Baek, JG 2019, Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process. in M Qiu (ed.), Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019., 8919603, Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 230-232, 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019, New York, United States, 19/8/1. https://doi.org/10.1109/CSE/EUC.2019.00051

Chang K, Baek JG. Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process. In Qiu M, editor, Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 230-232. 8919603. (Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019). doi: 10.1109/CSE/EUC.2019.00051

Chang, Kyuchang ; Baek, Jun Geol. / Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process. Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019. editor / Meikang Qiu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 230-232 (Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019).

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abstract = "Many studies using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. This is because sensor signals generated in the semiconductor production process provide important information for predicting quality and yield of the finished product. However, as the process becomes more sophisticated and refined, normal and abnormal data with similar shape appears. They only show delicate differences and it is difficult to classify them using general classification algorithms. The purpose of this research is to present a preprocessing methodology for improving classification performance. The methodology consists of four steps based on signal segmentation and clustering methods. The experimental results illustrate the better performance of the proposed procedure.",

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N2 - Many studies using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. This is because sensor signals generated in the semiconductor production process provide important information for predicting quality and yield of the finished product. However, as the process becomes more sophisticated and refined, normal and abnormal data with similar shape appears. They only show delicate differences and it is difficult to classify them using general classification algorithms. The purpose of this research is to present a preprocessing methodology for improving classification performance. The methodology consists of four steps based on signal segmentation and clustering methods. The experimental results illustrate the better performance of the proposed procedure.

AB - Many studies using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. This is because sensor signals generated in the semiconductor production process provide important information for predicting quality and yield of the finished product. However, as the process becomes more sophisticated and refined, normal and abnormal data with similar shape appears. They only show delicate differences and it is difficult to classify them using general classification algorithms. The purpose of this research is to present a preprocessing methodology for improving classification performance. The methodology consists of four steps based on signal segmentation and clustering methods. The experimental results illustrate the better performance of the proposed procedure.

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Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process

Abstract

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Keywords

ASJC Scopus subject areas

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