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Big data analytics for forecasting cycle time in semiconductor wafer fabrication system

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  • Junliang Wang
  • Jie Zhang

Abstract

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.

Suggested Citation

  • Junliang Wang & Jie Zhang, 2016. "Big data analytics for forecasting cycle time in semiconductor wafer fabrication system," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7231-7244, December.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:23:p:7231-7244
    DOI: 10.1080/00207543.2016.1174789
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    Cited by:

    1. Lixin Cheng & Qiuhua Tang & Zikai Zhang & Shiqian Wu, 2021. "Data mining for fast and accurate makespan estimation in machining workshops," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 483-500, February.
    2. M. López-Campos & F. Kristjanpoller & P. Viveros & R. Pascual, 2018. "Reliability Assessment Methodology for Massive Manufacturing Using Multi-Function Equipment," Complexity, Hindawi, vol. 2018, pages 1-8, February.
    3. Zilong Zhuang & Liangxun Guo & Zizhao Huang & Yanning Sun & Wei Qin & Zhao-Hui Sun, 2021. "DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2197-2207, December.
    4. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    5. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    6. Marvin Carl May & Alexander Albers & Marc David Fischer & Florian Mayerhofer & Louis Schäfer & Gisela Lanza, 2021. "Queue Length Forecasting in Complex Manufacturing Job Shops," Forecasting, MDPI, vol. 3(2), pages 1-17, May.
    7. Shijie Guo & Shufeng Tang & Dongsheng Zhang, 2019. "A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis," Complexity, Hindawi, vol. 2019, pages 1-21, November.
    8. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    9. Beixin Xia & Tong Tian & Yan Gao & Mingyue Zhang & Yunfang Peng, 2022. "A Dynamic Dispatching Method for Large-Scale Interbay Material Handling Systems of Semiconductor FAB," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    10. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    11. Wei Qin & Dongye Zha & Jie Zhang, 2020. "An effective approach for causal variables analysis in diesel engine production by using mutual information and network deconvolution," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1661-1671, October.
    12. Yu-Cheng Wang & Horng-Ren Tsai & Toly Chen, 2021. "A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication," Mathematics, MDPI, vol. 9(12), pages 1-18, June.

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