IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i1d10.1007_s10845-022-02042-8.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-02042-8
    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/s10845-022-02042-8?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. Pawlak, Zdzislaw, 2002. "Rough sets, decision algorithms and Bayes' theorem," European Journal of Operational Research, Elsevier, vol. 136(1), pages 181-189, January.
    2. 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.
    3. 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.
    4. 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.
    5. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    6. 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.
    7. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    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, Li-Fei & Tsai, Chih-Tsung, 2016. "Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 53(C), pages 197-206.
    2. 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.
    3. Wen-Min Lu & Qian Long Kweh & Chung-Wei Wang, 2021. "Integration and application of rough sets and data envelopment analysis for assessments of the investment trusts industry," Annals of Operations Research, Springer, vol. 296(1), pages 163-194, January.
    4. Fernandez del Pozo, J. A. & Bielza, C. & Gomez, M., 2005. "A list-based compact representation for large decision tables management," European Journal of Operational Research, Elsevier, vol. 160(3), pages 638-662, February.
    5. Nijkamp, Peter & Poot, Jacques, 2015. "Cultural Diversity: A Matter of Measurement," IZA Discussion Papers 8782, Institute of Labor Economics (IZA).
    6. Azam, Nouman & Zhang, Yan & Yao, JingTao, 2017. "Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets," European Journal of Operational Research, Elsevier, vol. 261(2), pages 704-714.
    7. Zaras, Kazimierz, 2001. "Rough approximation of a preference relation by a multi-attribute stochastic dominance for determinist and stochastic evaluation problems," European Journal of Operational Research, Elsevier, vol. 130(2), pages 305-314, April.
    8. Sung-Shun Weng & Yang Liu & Juan Dai & Yen-Ching Chuang, 2020. "A Novel Improvement Strategy of Competency for Education for Sustainable Development (ESD) of University Teachers Based on Data Mining," Sustainability, MDPI, vol. 12(7), pages 1-18, March.
    9. 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.
    10. Maurizio d’Amato, 2007. "Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies," International Real Estate Review, Global Social Science Institute, vol. 10(2), pages 42-65.
    11. Salvatore Barbagallo & Simona Consoli & Nello Pappalardo & Salvatore Greco & Santo Zimbone, 2006. "Discovering Reservoir Operating Rules by a Rough Set Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 19-36, February.
    12. Alessandro Scuderi & Luisa Sturiale & Giuseppe Timpanaro & Agata Matarazzo & Silvia Zingale & Paolo Guarnaccia, 2022. "A Model to Support Sustainable Resource Management in the “Etna River Valleys” Biosphere Reserve: The Dominance-Based Rough Set Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    13. Pawlak, Zdzislaw, 2004. "Decisions rules and flow networks," European Journal of Operational Research, Elsevier, vol. 154(1), pages 184-190, April.
    14. Andrew Schumann & Jan Woleński, 2015. "Decisions involving databases, fuzzy databases and codatabases," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 25(3), pages 59-72.
    15. Yongjun Ji & Zuhua Jiang & Xinyu Li & Yongwen Huang & Fuhua Wang, 2023. "A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1615-1637, April.
    16. Si-Hui Dong & Hui-Cheng Zhou & Hai-Jun Xu, 2004. "A Forecast Model of Hydrologic Single Element Medium and Long-Period Based on Rough Set Theory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 483-495, October.
    17. Liou, James J.H. & Yen, Leon & Tzeng, Gwo-Hshiung, 2010. "Using decision rules to achieve mass customization of airline services," European Journal of Operational Research, Elsevier, vol. 205(3), pages 680-686, September.
    18. Mi, Yunlong & Wang, Zongrun & Quan, Pei & Shi, Yong, 2024. "A semi-supervised concept-cognitive computing system for dynamic classification decision making with limited feedback information," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1123-1138.
    19. Fan, Shu-Kai S. & Chiu, Shang-Hao, 2024. "A new ViT-Based augmentation framework for wafer map defect classification to enhance the resilience of semiconductor supply chains," International Journal of Production Economics, Elsevier, vol. 273(C).
    20. Liao, Jui-Jung & Shih, Ching-Hui & Chen, Tai-Feng & Hsu, Ming-Fu, 2014. "An ensemble-based model for two-class imbalanced financial problem," Economic Modelling, Elsevier, vol. 37(C), pages 175-183.

    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:35:y:2024:i:1:d:10.1007_s10845-022-02042-8. 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.