Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 54, Issue 23, Pages 7231-7244Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2016.1174789
Keywords
cycle time; forecasting; big data; wafer fabrication
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Funding
- State Key Program of the National Natural Science Foundation of China [51435009]
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In order to improve the prompt delivery reliability of the semiconductor wafer fabrication system, a big data analytics (BDA) is designed to predict wafer lots' cycle time (CT), which is composed by four parts: data acquisition, data pre-processing, data analysing and data prediction. Firstly, the candidate feature set is constructed to collecting all features by analysing the material flow of wafer foundry. Subsequently, a data pre-processing technique is designed to extract, transform and load data from wafer lot transactions data-set. In addition, a conditional mutual information-based feature selection process is proposed to select key feature subset to reduce the dimension of data-set through data analysing without pre-knowledge. To handle the large volumes of data, a concurrent forecasting model is designed to predict the CT of wafer lots in parallel as well. According to the numerical analysis, the predict accuracy of the presented BDA improves clearly with the increase in data size. And, in the large-scale data-set, the BDA has higher accuracy than linear regression and back-propagation network in CT forecasting.
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