![](/images/graphics-bg.png)
Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory
Joint Authors
Tin, Tze Chiang
Chiew, Kang Leng
Phang, Siew Chee
Sze, San Nah
Tan, Pei San
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-02
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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, r.
American Psychological Association (APA)
Tin, Tze Chiang& Chiew, Kang Leng& Phang, Siew Chee& Sze, San Nah& Tan, Pei San. 2019. Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1129640
Modern Language Association (MLA)
Tin, Tze Chiang…[et al.]. Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1129640
American Medical Association (AMA)
Tin, Tze Chiang& Chiew, Kang Leng& Phang, Siew Chee& Sze, San Nah& Tan, Pei San. Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1129640
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129640