Segmented dynamic time warping based signal pattern classification

Jae Yeol Hong; Seung Hwan Park; Jun Geol Baek

doi:10.1109/CSE/EUC.2019.00058

Segmented dynamic time warping based signal pattern classification

Jae Yeol Hong, Seung Hwan Park, Jun Geol Baek

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

11 Citations (Scopus)

Abstract

The semiconductor manufacturing process is divided into fabrication process and packaging process. Fabrication process is a core process for manufacturing semiconductors and consists of about 700 unit processes. This unit process accumulates vast amounts of data, and many manufacturing companies apply data-based algorithms to manufacturing systems to improve process yield and quality. Data generated during the semiconductor manufacturing process from the process equipment is called fault detection and classification (FDC) trace data, and this data has time-series characteristics of different patterns depending on the sensor type or the recipe. Therefore, it is necessary to develop a classification algorithm appropriate to the signal pattern for process monitoring. In this paper, we develop segmented dynamic time warping technique which is specialized for process signal classification. Generally, it is known that dynamic time warping (DTW) has superior classification performance for time series data. However, there is a limit to classification that reflects the characteristics of semiconductor process signals. Therefore, we developed a classification algorithm for process signal data through segmented DTW using maximum overlap discrete wavelet transform (MODWT) and random sample consensus (RANSAC), and validated it.

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	263-265
Number of pages	3
ISBN (Electronic)	9781728116631
DOIs	https://doi.org/10.1109/CSE/EUC.2019.00058
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). This work was also supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University) and by the Samsung Electronics Co., Ltd.

Publisher Copyright:
© 2019 IEEE.

Keywords

-Fault-detection-and-classification-(FDC)
-Maximum-overlap-discrete-wavelet-transform-(MODWT)
-Random-sample-consensus-(RANSAC)
-Segmented-dynamic-time-warping-(SDTW)
-Semiconductor-manufacturing-process
Dynamic-time-warping-(DTW)

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.2019.00058

Cite this

Hong, J. Y., Park, S. H., & Baek, J. G. (2019). Segmented dynamic time warping based signal pattern classification. 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. 263-265). Article 8919528 (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.2019.00058

Segmented dynamic time warping based signal pattern classification. / Hong, Jae Yeol; Park, Seung Hwan; 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. 263-265 8919528 (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

Hong, JY, Park, SH & Baek, JG 2019, Segmented dynamic time warping based signal pattern classification. 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., 8919528, 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. 263-265, 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.2019.00058

Hong JY, Park SH, Baek JG. Segmented dynamic time warping based signal pattern classification. 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. 263-265. 8919528. (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.2019.00058

Hong, Jae Yeol ; Park, Seung Hwan ; Baek, Jun Geol. / Segmented dynamic time warping based signal pattern classification. 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. 263-265 (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 = "The semiconductor manufacturing process is divided into fabrication process and packaging process. Fabrication process is a core process for manufacturing semiconductors and consists of about 700 unit processes. This unit process accumulates vast amounts of data, and many manufacturing companies apply data-based algorithms to manufacturing systems to improve process yield and quality. Data generated during the semiconductor manufacturing process from the process equipment is called fault detection and classification (FDC) trace data, and this data has time-series characteristics of different patterns depending on the sensor type or the recipe. Therefore, it is necessary to develop a classification algorithm appropriate to the signal pattern for process monitoring. In this paper, we develop segmented dynamic time warping technique which is specialized for process signal classification. Generally, it is known that dynamic time warping (DTW) has superior classification performance for time series data. However, there is a limit to classification that reflects the characteristics of semiconductor process signals. Therefore, we developed a classification algorithm for process signal data through segmented DTW using maximum overlap discrete wavelet transform (MODWT) and random sample consensus (RANSAC), and validated it.",

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note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C2005949). This work was also supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University) and by the Samsung Electronics Co., Ltd. Publisher Copyright: {\textcopyright} 2019 IEEE.; 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 ; Conference date: 01-08-2019 Through 03-08-2019",

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Segmented dynamic time warping based signal pattern classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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