Title :
Big data predictive analtyics for proactive semiconductor equipment maintenance
Author :
Munirathinam, Sathyan ; Ramadoss, B.
Author_Institution :
Bus. Intell. Eng., Micron Technol., Inc., Boise, ID, USA
Abstract :
Manufacturing Industry generates about a third of all data today and the modern semiconductor manufacturing is one of the most contribution to this tsunami data volume. Terabytes of data is generated on a daily basis during ~500 steps in semiconductor chip processing. During this complex manufacturing process, equipment downtime may cause a significant loss of productivity and profit. In this paper, we are going to explore the predictive analytical algorithms and big data techniques in helping to achieve near-zero equipment downtime in the fabrication unit and to improve OEE (Overall Equipment Effectiveness), which is a key machine manufacturing productivity metric.
Keywords :
Big Data; data analysis; maintenance engineering; production engineering computing; productivity; semiconductor device manufacture; Big Data predictive analytics; OEE; data volume; equipment downtime; machine manufacturing productivity metric; manufacturing industry; overall equipment effectiveness; proactive semiconductor equipment maintenance; semiconductor chip processing; semiconductor manufacturing; Big data; Manufacturing processes; Predictive maintenance; Predictive models; Big Data; Equipment; OEE; Predictive Analytics (PA); Semiconductor;
Conference_Titel :
Big Data (Big Data), 2014 IEEE International Conference on
Conference_Location :
Washington, DC
DOI :
10.1109/BigData.2014.7004320
