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Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 smart production and an empirical study

Author

Listed:
  • Wenhan Fu

    (National Tsing Hua University)

  • Chen-Fu Chien

    (National Tsing Hua University
    Ministry of Science and Technology)

  • Lizhen Tang

    (National Tsing Hua University)

Abstract

Probe card that serves as the carrier of die and the transmitter of information is an indispensable test interface for integrated circuit testing. The probe card extracts the electrical signal of chip and sends it to the prober to screen defectives. In the process of interface transmission, signal disturbance or attenuation will lead to functional errors, and the output result will be different from the expected one to the screen of selected known good dies for integrated circuit packaging and final test. If abnormal situations happen with probe cards, the engineers will eliminate potential fault causes by trial and error method according to domain knowledge and personal experience for troubleshooting. As semiconductor industry is continuously migrating with shrinking feature size, the diagnosing and troubleshooting procedure for probe card is exponentially complicated and time-consuming. To enhance data integrity of circuit probe testing, this study aims to develop a Bayesian network for probe card fault diagnosis and troubleshooting via the integrated data-driven solutions considering potential rules derived from domain knowledge and manufacturing big data to empower Industry 3.5 smart production. An empirical study is conducted in a leading semiconductor testing company for validation. The experiment results show that the proposed approach can improve one-shot probability from 0.13 to 0.36 to improve one-shot success chance in troubleshooting process. The expected shooting times for probe faults have been reduced from 11 times to 3.96 times on average to save 63.97% of fault troubleshooting efforts in testing process. The proposed approach can provide effective suggestions to shorten troubleshooting time for yield improvement and subsequent packaging cost reduction to empower flexible decision-making for smart production. The results have shown practical viability of proposed approach for Industry 3.5. Indeed, the developed solutions have been implemented in real settings.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01680-0
    DOI: 10.1007/s10845-020-01680-0
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    References listed on IDEAS

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    1. Hänninen, Maria & Kujala, Pentti, 2012. "Influences of variables on ship collision probability in a Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 27-40.
    2. Chen-Fu Chien & Chiao-Wen Liu & Shih-Chung Chuang, 2017. "Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5095-5107, September.
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    4. Seokho Kang, 2020. "Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 319-326, February.
    5. 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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    Cited by:

    1. 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.

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