Remaining useful life prediction for ion etching machine cooling system using deep recurrent neural network-based approaches

Abstract

The cooling system called flowcool is an important part of the ion mill etching (IME) machine. In the case of the cooling system failure during operation, it will lead to significant impacts on the final quality of wafers. To address this problem, effective maintenance plans are made according to the predicted time to failure, i.e., the remaining useful life (RUL). However, the RUL prediction performances of existing prognostic approaches for the flowcool system are not satisfactory. In this work, multiple deep recurrent neural network-based approaches are used for RUL prediction. To improve reliability, the random forest-based early degradation warning approach is employed before the RUL prediction. The long short-term memory (LSTM) neural network, the gated recurrent unit (GRU) neural network, and the additional fully-connected layers (FCs) are used to predict the RUL of flowcool respectively. Results of the comparison study on the dataset of the 2018 PHM Data Challenge competition show that the proposed GRU and GRU-FCs based approaches integrated with sample screening, new feature construction, and degradation warning outperform the other reported approaches. The symmetric mean absolute percentage errors (SMAPE) of the RUL prediction in 3 failure modes achieved 14.13%, 14.44%, and 25.63% respectively.