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

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ID: 85536
2019
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Abstract
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). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, .
Reference Key
tin2019incomingcomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tin, Tze Chiang;Chiew, Kang Leng;Phang, Siew Chee;Sze, San Nah;Tan, Pei San;
Journal Computational Intelligence and Neuroscience
Year 2019
DOI
10.1155/2019/8729367
URL
Keywords
finite element model mindlin’s theory moving load triple-layer composite plate

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