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Study on IoT and Big Data Analysis of Furnace Process Exhaust Gas Leakage

  • Yu-Wen Zhou
  • Kuo-Chi ChangEmail author
  • Jeng-Shyang Pan
  • Kai-Chun Chu
  • Der-Juinn Horng
  • Yuh-Chung Lin
  • Huang Jing
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 156)

Abstract

Modern FABs use a large number of high-energy processes such as plasma, CVD, and ion implantation; the furnace is one of the important tools of semiconductor manufacturing. The FAB installed FTIR system due to the 12” furnace tools based on the aforementioned production management requirements. This study used open-type FTIR and integrated IoT mechanism to connect to the cloud, which is suitable for a variety of gaseous pollutants. This study set up two measuring points of furnace process tools in the 12” factory of Hsinchu Science Park in Taiwan. This study obtained FTIR measurements, and according to the OHSA regulations, this study is set in the cloud database for big data analysis and decision-making, when the upper limits of TEOS, C2H4, and CO are 0.6 ppm, 2.0 ppm, and 1.7 ppm and the lower limits of TEOS, C2H4, and CO are 0.4 ppm, 1.5 ppm, and 1 ppm. The application architecture of this study can be extended to other semiconductor processes, so that IoT integration and big data operations can be performed for all processes; this is an important step in promoting FAB intelligent production and an important contribution of this study.

Keywords

IoT Big data Furnace Exhaust gas Gas leakage 

References

  1. 1.
    Lu, C.C., Chang, K.C., Chen, C.Y.: Study of high-tech process furnace using inherently safer design strategies (IV). The advanced thin film manufacturing process design and adjustment. J. Loss Prev. Process Ind. 40, 378–395 (2016)CrossRefGoogle Scholar
  2. 2.
    Lu, C.C., Chang, K.C., Chen, C.Y. Study of high-tech process furnace using inherently safer design strategies (III) advanced thin film process and reduction of power consumption control. J. Loss Prev. Process Ind. 43, 280–291 (2016)CrossRefGoogle Scholar
  3. 3.
    Sze, S.M., Ng, K.K.: Physics of Semiconductor Devices, 3rd edn. Wiley (2006). ISBN: 978-0-471-14323-9Google Scholar
  4. 4.
    Pan, J.-S., et al.: α-Fraction first strategy for hierarchical model in wireless sensor networks. J. Internet Technol. 19(6) (2018). Papers (ISSN 1607-9264)Google Scholar
  5. 5.
    Wu, H.-T., Hu, W.-C., Chou, T.-Y., Lin, J.-J.: A clownfish farming monitoring system based on the internet of things. J. Netw. Intell. 2(2), 213–230 (2017)Google Scholar
  6. 6.
    Chen, C.Y., Wang, C.J., Chen, E., Wu, C.K., Yang, Y.K., Wang, J.S., Chung, P.C.: Detecting sustained attention during cognitive work using heart rate variability. In: IEEE Conference on In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 372–375 (2010)Google Scholar
  7. 7.
    Mattana, G., Briand, D., Marette, A., Vásquez Quintero, A., de Rooij, N.F.: Polylactic acid as a biodegradable material for all-solution-processed organic electronic devices. Organ. Electron. 17, 77–86 (2015)CrossRefGoogle Scholar
  8. 8.
    Wang, X.C.: Li Yang. Infrared suppression of submarine exhaust system, laser and infrared 39(4), 393–396 (2009)Google Scholar
  9. 9.
    Wang, X.C., Guo H.X., Pan, L. et al.: Comparisons on flow and temperature fields for water-collection box of diesel exhaust system. In: Proceedings of the 2nd International Conference on Manufacturing Science and Engineering. Trans Tech Publications, Guilin (2011)CrossRefGoogle Scholar
  10. 10.
    Nguyen, T.-T., Dao, T.-K., Pan, J.-S., Horng, M.-F., Shieh, C.-S.: An improving data compression capability in sensor node to support SensorML-Compatible for Internet-of-Things. J. Netw. Intell. 3(2), 74–90 (2018)Google Scholar
  11. 11.
    Holzer, F., Kopinke, F.-D., Roland, U.: Non-thermal plasma treatment for the elimination of odorous compounds from exhaust air from cooking processes. J. Chem. Eng. 334, 1988–1995 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yu-Wen Zhou
    • 1
  • Kuo-Chi Chang
    • 1
    Email author
  • Jeng-Shyang Pan
    • 1
  • Kai-Chun Chu
    • 2
  • Der-Juinn Horng
    • 2
  • Yuh-Chung Lin
    • 1
  • Huang Jing
    • 3
  1. 1.Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina
  2. 2.Department of Business Administration Group of Strategic ManagementNational Central UniversityTaoyuanTaiwan
  3. 3.College of Information Science and EngineeringFujian University of TechnologyFuzhouChina

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