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Using intelligent technology and real-time feedback algorithm to improve manufacturing process in IoT semiconductor industry

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Abstract

To enable the Internet of things, the semiconductor manufacturing process has progressed from the micron to the deep submicron level. Quality improvement is one of the great challenges in wafer fabrication. Computer-integrated manufacturing (CIM) has arisen as a means by which to reduce wafer rework and continuously improve the semiconductor production process. This study uses statistical process control (SPC) and data mining technology to analyze the collected semiconductor process data. A real-time feedback algorithm is employed to ensure that each product lot is manufactured using optimized process parameters. This maximizes production capability, increases the semiconductor yield rate and reduces the cost of manufacturing. This paper focuses on wafer fabrication facilities (often called “fabs,” or “foundries”). The data mining architecture is implemented between CIM and the manufacturing execution system. Association rules and the k-means clustering algorithm are used together with real-time feedback control analysis to extract and analyze each parameter that affects the semiconductor production yield. This combination of real-time feedback and SPC using historical process data allows the system to predict the optimum process parameters for the next lot. The system compensates dynamically to accommodate differences among various machines and products, giving each machine a level of flexibility in manufacturing the product. The proposed semiconductor system can be applied to traditional manufacturing industry process analysis. Our results show how our system can improve the semiconductor manufacturing process in terms of processing capability, yield rate, stability and flexibility, while reducing costs, thus creating a competitive advantage for the wafer fab.

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Acknowledgements

This work was supported by the Scientific Research Fund of Key Project of Quality Engineering Teaching Reform in 2020 by Dongguan Vocational and Technical College (JGZD202022), this work was also supported by the Scientific Research Fund of Dongguan Polytechnic (2019c03). Also Key scientific research platforms and projects of colleges and universities in 2020 of Education Department of Guangdong Province (No.2020ZDZX3109).

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Authors and Affiliations

  1. Department of Computer Engineering, Dongguan Polytechnic, Dongguan, 523808, China

    Bin Li & Ruey-Shun Chen

  2. Department of Information Science, National Chiao Tung University, Hsinchu, 30050, Taiwan

    C.-Y. Liu

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  1. Bin Li
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  2. Ruey-Shun Chen
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  3. C.-Y. Liu
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Correspondence to Ruey-Shun Chen.

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Li, B., Chen, RS. & Liu, CY. Using intelligent technology and real-time feedback algorithm to improve manufacturing process in IoT semiconductor industry. J Supercomput 77, 4639–4658 (2021). https://doi.org/10.1007/s11227-020-03457-x

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