Big data predictive analtyics for proactive semiconductor equipment maintenance

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.