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Bayesian inference for multi-label classification for root cause analysis and probe card maintenance decision support and an empirical study

Author

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  • Chen-Fu Chien

    (National Tsing Hua University
    National Tsing Hua University
    National Science and Technology Council)

  • Jia-Yu Peng

    (National Tsing Hua University)

Abstract

Probe cards have been employed as intermediary tools between the wafers and automatic test equipment to prevent defect dies from entering the packaging stage to reduce the consumer risk and extra losses. To enhance the partnership, probe card manufacturer is responsible for troubleshooting of the sold probe cards in short time as part of after-sales maintenance to enhance the productivity. In practice, maintenance engineers relied on domain knowledge for probe card maintenance. However, owing the continuously migration of advanced technologies for semiconductor manufacturing and exponentially increasing product complexity, probe card maintenance has become challenging and time-consuming. Most of the existing studies have not addressed the root cause analysis problem for identifying corrective actions for abnormal symptoms, nor considered the effectiveness of maintenance. The selection of appropriate maintenance actions is crucial in root cause analysis. To fill the gaps, this study integrates domain knowledge and data-driven approaches to develop a smart maintenance solution based on Bayesian inference for multi-label classification to derive effective suggestions to enhance after-sales service quality for probe cards. An empirical study was conducted in a leading probe card company for validation. The results have shown practical viability of the developed solution to effectively and efficiently generate a number of maintenance recommendations for the engineers to improving troubleshooting efficiency and service quality while reducing maintenance time and machine downtime.

Suggested Citation

  • Chen-Fu Chien & Jia-Yu Peng, 2025. "Bayesian inference for multi-label classification for root cause analysis and probe card maintenance decision support and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1943-1958, March.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02336-z
    DOI: 10.1007/s10845-024-02336-z
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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.
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    4. Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
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