Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

• Main Authors: Tin, Tze Chiang, Chiew, Kang Leng, Phang, Siew Chee, Sze, San Nah, Tan, Pei San
• Format: Article
• Language: English
• Published: Hindawi Publishing 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://ir.unimas.my/id/eprint/29616/1/Chiew.pdf; http://ir.unimas.my/id/eprint/29616/; http://downloads.hindawi.com/journals/cin/2019/8729367.pdf
• Institution: Universiti Malaysia Sarawak

Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). -erefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. -e current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-inProgress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-InProgress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. -e proposed model’s prediction results were compared with the results of the current statistical forecasting method of the Fab. -e experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson’s correlation coefficient.