IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i3d10.1007_s10845-020-01680-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01680-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01680-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. Eduardo Oliveira & Vera L. Miguéis & José L. Borges, 2023. "Automatic root cause analysis in manufacturing: an overview & conceptualization," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2061-2078, June.
    3. Chen-Fu Chien & Chung-Jen Kuo & Chih-Min Yu, 2020. "Tool allocation to smooth work-in-process for cycle time reduction and an empirical study," Annals of Operations Research, Springer, vol. 290(1), pages 1009-1033, July.
    4. Sangho Lee & Youngdoo Son, 2021. "Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-21, June.
    5. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    6. Carine Dominguez-Péry & Lakshmi Narasimha Raju Vuddaraju & Isabelle Corbett-Etchevers & Rana Tassabehji, 2021. "Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-32, December.
    7. Zhang, Yang & Sun, Xukai & Chen, Jihong & Cheng, Cheng, 2021. "Spatial patterns and characteristics of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    8. Nduhura Munga, Justin & Dauzère-Pérès, Stéphane & Yugma, Claude & Vialletelle, Philippe, 2015. "A mathematical programming approach for optimizing control plans in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 160(C), pages 213-219.
    9. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    10. Yuan Chen & Zhijie Zhou & Lihao Yang & Guanyu Hu & Xiaoxia Han & Shuaiwen Tang, 2022. "A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method," Journal of Risk and Reliability, , vol. 236(3), pages 458-476, June.
    11. Guizhen Zhang & Vinh V. Thai & Adrian Wing‐Keung Law & Kum Fai Yuen & Hui Shan Loh & Qingji Zhou, 2020. "Quantitative Risk Assessment of Seafarers’ Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 8-23, January.
    12. Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    13. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    14. Bing Wu & Huibin Tian & Xinping Yan & C. Guedes Soares, 2020. "A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks," Journal of Risk and Reliability, , vol. 234(2), pages 422-436, April.
    15. Weiliang Qiao & Yu Liu & Xiaoxue Ma & Yang Liu, 2020. "Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 957-980, May.
    16. Fan, Shiqi & Blanco-Davis, Eduardo & Yang, Zaili & Zhang, Jinfen & Yan, Xinping, 2020. "Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    17. Çakır, Erkan & Fışkın, Remzi & Sevgili, Coşkan, 2021. "Investigation of tugboat accidents severity: An application of association rule mining algorithms," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    18. 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.
    19. Guo, Yunlong & Jin, Yongxing & Hu, Shenping & Yang, Zaili & Xi, Yongtao & Han, Bing, 2023. "Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01680-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.