DEVELOPMENT OF SEQUENTIAL ASSOCIATION RULES FOR PREVENTING MINOR-STOPPAGES IN SEMI-CONDUCTOR MANUFACTURING

Sumika Arima, Ushio Sumita, Jun Yoshii

2012

Abstract

In semi-conductor manufacturing, the machine downtimes due to minor-stoppages often exceed 40% of the working hours of a day, and would amount to the huge loss. However, effective methodological tools for predicting and preventing the minor-stoppages are hard to come by. The purpose of this research is to fill this gap by establishing effective preventive maintenance policies for controlling minor-stoppages. Our approach is to develop association rules based on sequential data along the time axis so that the resulting rules could be used for predicting occurrences of certain minor-stoppages. The proposed methodology is applied to a real data set and yields two preventive maintenance policies in a concrete form, thereby demonstrating its power and usefulness. While the paper focuses on the testing process, the methodology proposed in this paper is valid for other production processes, provided that similar sequential data could be collected.

References

  1. Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216.
  2. Agrawal, R. and Srikant, R. (1994). Fast algoritms for mining association rules. Proceedings of the 20th Conference on Very Large Data Bases, pages 478-499.
  3. Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. Proceedings of the International Conference on Data Engineering.
  4. Chen, W., Tseng, S., and Wang, C. (2005). A novel manufacturing defect detection method using association rule mining techniques. Expert Systems with Applications, 29:807-815.
  5. Chien, C., Wang, W., and Cheng, J. (2007). Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems with Applications, 33:192-198.
  6. Gardner, M. and Bieker, J. (2000). Data mining solves tough semiconductor manufacturing problems. Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  7. Jiang, N. and Gruenwald, L. (2006). Research issues in data stream association rule mining. ACM SIGMOD Record, 35(1):14-19.
  8. Lu, H., Han, J., and Feng, L. (1998). Stock movement prediction and n-dimensional inter-transaction association rules. Proceedings of the 1998 ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pages 12:1-12:7.
  9. Qin, L. and Shi, Z. (2006). Efficiently mining association rules from time series. International Journal of Information Technology, 12(4):30-38.
Download


Paper Citation


in Harvard Style

Arima S., Sumita U. and Yoshii J. (2012). DEVELOPMENT OF SEQUENTIAL ASSOCIATION RULES FOR PREVENTING MINOR-STOPPAGES IN SEMI-CONDUCTOR MANUFACTURING . In Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-8425-97-3, pages 349-354. DOI: 10.5220/0003713503490354


in Bibtex Style

@conference{icores12,
author={Sumika Arima and Ushio Sumita and Jun Yoshii},
title={DEVELOPMENT OF SEQUENTIAL ASSOCIATION RULES FOR PREVENTING MINOR-STOPPAGES IN SEMI-CONDUCTOR MANUFACTURING},
booktitle={Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2012},
pages={349-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003713503490354},
isbn={978-989-8425-97-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - DEVELOPMENT OF SEQUENTIAL ASSOCIATION RULES FOR PREVENTING MINOR-STOPPAGES IN SEMI-CONDUCTOR MANUFACTURING
SN - 978-989-8425-97-3
AU - Arima S.
AU - Sumita U.
AU - Yoshii J.
PY - 2012
SP - 349
EP - 354
DO - 10.5220/0003713503490354