Sparse coding-based failure prediction for prudent operation of LED manufacturing equipment

Abstract

A sudden failure of a critical component in light-emitting diode (LED) manufacturing equipment would result in unscheduled downtime, leading to a possibly significant loss in productivity for the manufacturer. It is therefore important to be able to predict upcoming failures. A major obstacle to failure prediction is the limited amount of equipment lifecycle data available for training, as equipment failure is not expected to be frequent. This calls for machine learning techniques capable of making accurate failure predictions with limited training data. This paper describes such a method based on sparse coding. We demonstrate the prediction performance of the method on a real-world dataset from LED manufacturing equipment. We show that sparse coding can draw out salient features associated with failure cases, and can thus produce accurate failure predictions. We also analyze how sparse coding-based failure prediction can lead to significant efficiency improvements in equipment operation.