TY - JOUR
T1 - A Clustering-Based Equipment Condition Model of Chemical Vapor Deposition Process
AU - Yoo, Youngji
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 supported by BK21 Plus program (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd. Also, this work was supported by a Korea University Grant.
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 supported by BK21 Plus program (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd.?Also, this work was supported by a Korea University Grant.
Publisher Copyright:
© 2019, Korean Society for Precision Engineering.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - In semiconductor manufacturing, equipment condition monitoring is important to improve the efficiency of the manufacturing process by performing equipment maintenance in a timely manner. In this paper, we propose the clustering-based equipment condition model to select key sensors relating to a maintenance. During the manufacturing process, huge amounts of data are collected in real time from sensors on the equipment. The sensor data has various patterns, such as increased pattern, decreased pattern, unchanged pattern, and other patterns. We apply five clustering algorithms to group the sensors with similar characteristics and extract key sensors that are highly correlated with equipment health condition. The health condition monitoring model consists of the combination of key sensors. To validate proposed method, the empirical study is conducted using collected sensor data from a chemical vapor deposition (CVD) process in a semiconductor company in the Republic of Korea. The model with clustered sensors outperforms the model with full sensors. The health condition monitoring model assists engineers in making decisions regarding the equipment maintenance.
AB - In semiconductor manufacturing, equipment condition monitoring is important to improve the efficiency of the manufacturing process by performing equipment maintenance in a timely manner. In this paper, we propose the clustering-based equipment condition model to select key sensors relating to a maintenance. During the manufacturing process, huge amounts of data are collected in real time from sensors on the equipment. The sensor data has various patterns, such as increased pattern, decreased pattern, unchanged pattern, and other patterns. We apply five clustering algorithms to group the sensors with similar characteristics and extract key sensors that are highly correlated with equipment health condition. The health condition monitoring model consists of the combination of key sensors. To validate proposed method, the empirical study is conducted using collected sensor data from a chemical vapor deposition (CVD) process in a semiconductor company in the Republic of Korea. The model with clustered sensors outperforms the model with full sensors. The health condition monitoring model assists engineers in making decisions regarding the equipment maintenance.
KW - Chemical vapor deposition (CVD) process
KW - Clustering
KW - Health condition monitoring
KW - Predictive maintenance
KW - Semiconductor manufacturing process
UR - http://www.scopus.com/inward/record.url?scp=85068822612&partnerID=8YFLogxK
U2 - 10.1007/s12541-019-00177-y
DO - 10.1007/s12541-019-00177-y
M3 - Article
AN - SCOPUS:85068822612
SN - 2234-7593
VL - 20
SP - 1677
EP - 1689
JO - International Journal of Precision Engineering and Manufacturing
JF - International Journal of Precision Engineering and Manufacturing
IS - 10
ER -