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

• Main Authors: Chen-Fu Chien, Alejandra Campero Diaz, Yu-Bin Lan
• Format: Article
• Language: English
• Published: Atlantis Press 2014-07-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Overall Usage Effectiveness; Data Mining; Manufacturing Intelligence; Decision Tree; Cost Reduction; Semiconductor Manufacturing
• Online Access: https://www.atlantis-press.com/article/25868570.pdf

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.