International Journal of Computational Intelligence Systems

Volume 7, Issue sup2, July 2014, Pages 52 - 65

A data mining approach for analyzing semiconductor MES and FDC data to enhance overall usage effectiveness (OUE)

Authors
Chen-Fu Chien, Alejandra Campero Diaz, Yu-Bin Lan
Corresponding Author
Chen-Fu Chien
Available online July 2014.
DOI
https://doi.org/10.1080/18756891.2014.947114How to use a DOI?
Keywords
Overall Usage Effectiveness, Data Mining, Manufacturing Intelligence, Decision Tree, Cost Reduction, Semiconductor Manufacturing
Abstract
Wafer fabrication is a complex and lengthy process that involves hundreds of process steps with monitoring numerous process parameters at the same time for yield enhancement. Big data is automatically collected during manufacturing processes in modern wafer fabrication facility. Thus, potential useful information can be extracted from big data to enhance decision quality and enhance operational effectiveness. This study aims to develop a data mining framework that integrates FDC and MES data to enhance the overall usage effectiveness (OUE) for cost reduction. We validated this approach with an empirical study in a semiconductor company in Taiwan. The results demonstrated the practical viability of this approach. The extracted information and knowledge is helpful to engineers for identifying the major tools factors affecting indirect material usage effectiveness and identify specific periods of time when a functional tool has abnormal usage of material.
Copyright
© The authors.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Cite this article

TY  - JOUR
AU  - Chien, Chen-Fu
AU  - Diaz, Alejandra Campero
AU  - Lan, Yu-Bin
DA  - 2014/07/01
TI  - A data mining approach for analyzing semiconductor MES and FDC data to enhance overall usage effectiveness (OUE)
JO  - International Journal of Computational Intelligence Systems
SP  - 52
EP  - 65
VL  - 7
IS  - sup2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.947114
DO  - https://doi.org/10.1080/18756891.2014.947114
ID  - Chien2014
ER  -