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 | |
| 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 | United States |
| City | New York |
| Period | 19/8/1 → 19/8/3 |
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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
Cite this
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
}
TY - GEN
T1 - Univiariate signal preprocessing methodology for fault detection in semiconductor manufacturing process
AU - Chang, Kyuchang
AU - Baek, Jun Geol
PY - 2019/8
Y1 - 2019/8
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.
KW - Time Series Classification(TSC) Feature Extraction Fault Detection and Classification(FDC) Clustering Hierarchical Clustering Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85077070768&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077070768&partnerID=8YFLogxK
U2 - 10.1109/CSE/EUC.2019.00051
DO - 10.1109/CSE/EUC.2019.00051
M3 - Conference contribution
AN - SCOPUS:85077070768
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 - 230
EP - 232
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.
ER -