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

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.

Big data analytics in supply chain management: A state-of-the-art literature review

“…Classification is the most common approach at the predictive analytics level and has been widely applied in manufacturing to support production planning and control (Chien et al, 2014; and equipment maintenance and diagnosis Shu et al, 2016;. This type of BDA model also plays a key role in logistics/transportation (Li et al, 2014;Yu and AbdelAty, 2014;Zangenehpour et al, 2015) and procurement research (Ling Ho and Wen Shih, 2014;Mori et al, 2012) but apparently, current studies in those areas have not fully exploited the advantages of classification.…”

“…The review also identifies some versatile techniques that can be adapted to different types of models. For instance, Kmeans clustering algorithm is among the most adaptable techniques as it can be adopted in clustering (St-Aubin et al, 2015;Tan and Lee, 2015), classification (Chien et al, 2014), forecasting (Stefanovic, 2015), and modelling and simulation (Lei and Moon, 2015). In those studies, K-means is often performed in the initial phase of the data analytics process to partition the raw heterogeneous datasets into more homogenous segments.…”

“…Product R&D Bae and Kim, 2011;Do, 2014;Lei and Moon, 2015;Opresnik and Taisch, 2015;Zhang et al, 2017 Production planning & control Chien et al, 2014;Krumeich et al, 2016;Zhang et al, 2017;Shu et al, 2016;Dai et al, 2012;Zhong et al, 2016;Helo and Hao, 2017 Quality management Krumeich et al, 2016;Zhang et al, 2017; Maintenance & diagnosis Shu et al, 2016;Guo et al, 2016;Kumar et al, 2016;Zhang et al, 2017;Wang et al, 2015 Warehousing Storage assignment Chuang et al, 2014;Li, Moghaddam, et al, 2016;Tsai and Huang, 2015;Chiang et al, 2011;Chiang et al, 2014 Order picking Ballestín et al, 2013;Chuang et al, 2014; Inventory control Alyahya et al, 2016;Hofmann, 2015;Huang and Van Mieghem, 2014;Lee et al, 2016;Stefanovic, 2015 Logistics and transportation Cui et al, 2016;Li et al, 2015;Shi and Abdel-Aty, 2015;StAubin et al, 2015;Toole et al, 2015;Xia et al, 2016;Yu and Abdel-Aty, 2014;Zangenehpour et al, 2015;…”

Business Intelligence in Industry 4.0: State of the art and research opportunities

“…Chien et al [9] Data mining for production process improvement Room for model further improvement Information…”

Model-Based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

“…The potential impacts of data analytics on manufacturing-systems efficiency include a reduction of production cost and time across all manufacturing levels [1,2]. Data scientists and manufacturing engineers often collaborate when using data analytics to solve process-specific problems to improve product quality [3,4], equipment efficiency [5,6], and resource efficiency [7,8]. However, these collaborations require a significant amount of time and effort to merge the expertise from these two domains.…”