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)
- Chen-Fu Chien, Alejandra Campero Diaz, Yu-Bin Lan
- Corresponding Author
- Chen-Fu Chien
Available Online 9 January 2017.
- https://doi.org/10.1080/18756891.2014.947114How to use a DOI?
- Overall Usage Effectiveness, Data Mining, Manufacturing Intelligence, Decision Tree, Cost Reduction, Semiconductor Manufacturing
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Chen-Fu Chien AU - Alejandra Campero Diaz AU - Yu-Bin Lan PY - 2017 DA - 2017/01 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 - Chien2017 ER -