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Condition monitoring and prediction of solution quality during a copper electroplating process

Author

Listed:
  • Gerardo Emanuel Granados

    (INP-ENIT)

  • Loïc Lacroix

    (INP-ENIT)

  • Kamal Medjaher

    (INP-ENIT)

Abstract

This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method.

Suggested Citation

  • Gerardo Emanuel Granados & Loïc Lacroix & Kamal Medjaher, 2020. "Condition monitoring and prediction of solution quality during a copper electroplating process," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 285-300, February.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1445-4
    DOI: 10.1007/s10845-018-1445-4
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    References listed on IDEAS

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    1. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    2. Ahmed Ragab & Mohamed-Salah Ouali & Soumaya Yacout & Hany Osman, 2016. "Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 943-958, October.
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    Cited by:

    1. Jinping Liu & Jie Wang & Xianfeng Liu & Tianyu Ma & Zhaohui Tang, 2022. "MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1255-1271, June.

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