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Nonstationary signal analysis and support vector machine based classification for vibration based characterization and monitoring of slit valves in semiconductor manufacturing

Author

Listed:
  • M. Musselman

    (Lam Research Corporation)

  • H. Xie

    (University of Texas)

  • D. Djurdjanovic

    (University of Texas)

Abstract

Slit valves play an important role in semiconductor manufacturing, enabling creation and maintaining of a vacuum environment required for wafer processing. Due to the high volume of production in the modern semiconductor industry, slit valves could experience severe degradation over their lifetime. If maintenance is not applied in due time, degraded valves may lead to defects in finished products due to pressure loss and particle generation. In this paper, we propose methods for signal processing and feature extraction for analysis of slit valve vibration signals. These methods are then used to demonstrate the ability to reliably, accurately and efficiently distinguish between vibration patterns of each individual valve via a multi-class classification procedure. Furthermore, instantaneous time–frequency entropy of valve vibrations enabled long term monitoring of a slit valve in production, in spite of variations in valve speed and operations.

Suggested Citation

  • M. Musselman & H. Xie & D. Djurdjanovic, 2019. "Nonstationary signal analysis and support vector machine based classification for vibration based characterization and monitoring of slit valves in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1099-1110, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1308-4
    DOI: 10.1007/s10845-017-1308-4
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    Cited by:

    1. Yu Wang & Mingkai Zhang & Xiaowei Tang & Fangyu Peng & Rong Yan, 2022. "A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1483-1502, June.

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