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Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study

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Listed:
  • Chen-Fu Chien

    (National Tsing Hua University
    Ministry of Science & Technology)

  • Hsin-Jung Wu

    (National Tsing Hua University)

Abstract

Wafer probe test plays a crucial role to distinguish the good dies from the remaining defected dies on the wafers via the probe card as the testing signal interface between the tester and the integrated circuits on the fabricated wafers. Unexpected probe card failures that happen during the testing process will affect testing quality, reduce overall equipment efficiency and productivity. In practice, the engineers rely on domain knowledge and the process of trial and error for fault diagnosis and troubleshooting. However, as the IC device features are continuously shrinking with an increasing number and density of the bond pads of the circuits on the wafer, fault diagnosis and troubleshooting for probe card have become complicated and time-consuming. To fill the gap, this study aims to develop a data-driven framework that integrates rough set theory and domain knowledge to derive effective decision rules to enhance the decision quality and efficiency for advanced quality control and smart manufacturing. An empirical study was conducted in a leading semiconductor testing company in Taiwan for validation. The proposed framework can shorten fault diagnosis procedure and enhance productivity, while enhancing probing test integrity to reduce both the producer risk and customer risk. The developed solution is implemented in real setting.

Suggested Citation

  • Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02042-8
    DOI: 10.1007/s10845-022-02042-8
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    References listed on IDEAS

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    1. Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
    2. Hsu, Shao-Chung & Chien, Chen-Fu, 2007. "Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 107(1), pages 88-103, May.
    3. 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.
    4. Pawlak, Zdzislaw, 2002. "Rough sets, decision algorithms and Bayes' theorem," European Journal of Operational Research, Elsevier, vol. 136(1), pages 181-189, January.
    5. Wenhan Fu & Chen-Fu Chien & Lizhen Tang, 2022. "Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 smart production and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 785-798, March.
    6. Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.
    7. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    8. Chien, Chen-Fu & Wang, Hung-Ju & Wang, Min, 2007. "A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness," International Journal of Production Economics, Elsevier, vol. 107(1), pages 20-30, May.
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