TY - GEN
T1 - Segmented dynamic time warping based signal pattern classification
AU - Hong, Jae Yeol
AU - Park, Seung Hwan
AU - Baek, Jun Geol
N1 - 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.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - -Fault-detection-and-classification-(FDC)
KW - -Maximum-overlap-discrete-wavelet-transform-(MODWT)
KW - -Random-sample-consensus-(RANSAC)
KW - -Segmented-dynamic-time-warping-(SDTW)
KW - -Semiconductor-manufacturing-process
KW - Dynamic-time-warping-(DTW)
UR - http://www.scopus.com/inward/record.url?scp=85077035687&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077035687&partnerID=8YFLogxK
U2 - 10.1109/CSE/EUC.2019.00058
DO - 10.1109/CSE/EUC.2019.00058
M3 - Conference contribution
AN - SCOPUS:85077035687
T3 - Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
SP - 263
EP - 265
BT - Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
A2 - Qiu, Meikang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
Y2 - 1 August 2019 through 3 August 2019
ER -