IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i1d10.1007_s10845-023-02242-w.html
   My bibliography  Save this article

Shape prior guided defect pattern classification and segmentation in wafer bin maps

Author

Listed:
  • Rui Wang

    (Harbin Institute of Technology)

  • Songhao Wang

    (Southern University of Science and Technology)

  • Ben Niu

    (Shenzhen University)

Abstract

In semiconductor manufacturing, patterns formed by defective dies in a wafer bin map (WBM) reveal possible problems during the wafer fabrication process. Therefore, the identification of these patterns is important for root cause diagnosis and yield enhancement. Recently, as the manufacturing process becomes increasingly complicated, mixed-type defect patterns have been frequently observed on the WBMs. The joint classification and segmentation of each pattern contained in the mixed-type pattern is challenging, especially when these patterns are connected or overlapped. This study proposes a shape prior guided method in which the shape templates are deformed to match the patterns with a spatial transformer network. The deformation based method is able to give plausible results while reducing computation cost of the network. Furthermore, the method is flexible and easily extended to deal with complex shapes by defining different shape templates. The experimental results demonstrate the effectiveness of the proposed method in the identification of the pattern types, as well as the separation of each isolated pattern.

Suggested Citation

  • Rui Wang & Songhao Wang & Ben Niu, 2025. "Shape prior guided defect pattern classification and segmentation in wafer bin maps," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 319-330, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02242-w
    DOI: 10.1007/s10845-023-02242-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02242-w
    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/s10845-023-02242-w?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. Minghao Piao & Cheng Hao Jin, 2023. "CNN and ensemble learning based wafer map failure pattern recognition based on local property based features," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3599-3621, December.
    2. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    3. Tongwha Kim & Kamran Behdinan, 2023. "Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3215-3247, December.
    4. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
    5. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    6. Jinho Kim & Youngmin Lee & Heeyoung Kim, 2018. "Detection and clustering of mixed-type defect patterns in wafer bin maps," IISE Transactions, Taylor & Francis Journals, vol. 50(2), pages 99-111, February.
    7. Yuan, Tao & Kuo, Way, 2008. "Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference," European Journal of Operational Research, Elsevier, vol. 190(1), pages 228-240, October.
    8. Tae San Kim & Jong Wook Lee & Won Kyung Lee & So Young Sohn, 2022. "Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1715-1724, August.
    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. Shijie Wang & Haiyong Chen & Kun Liu & Ying Zhou & Huichuan Feng, 2023. "Meta-FSDet: a meta-learning based detector for few-shot defects of photovoltaic modules," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3413-3427, December.
    2. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    3. Tongwha Kim & Kamran Behdinan, 2023. "Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3215-3247, December.
    4. Radu Cristian Alexandru Iacob & Vlad Cristian Monea & Dan Rădulescu & Andrei-Florin Ceapă & Traian Rebedea & Ștefan Trăușan-Matu, 2020. "AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    5. Xueying Zhang & Qinbao Song, 2015. "A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-30, April.
    6. Junming Yin & Jerry Luo & Susan A. Brown, 2021. "Learning from Crowdsourced Multi-labeling: A Variational Bayesian Approach," Information Systems Research, INFORMS, vol. 32(3), pages 752-773, September.
    7. Chengjun Xu & Guobin Zhu, 2021. "Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 237-249, January.
    8. Shouhong Chen & Zhentao Huang & Tao Wang & Xingna Hou & Jun Ma, 2025. "Wafer map defect recognition based on multi-scale feature fusion and attention spatial pyramid pooling," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 271-284, January.
    9. Parag Parashar & Chun Han Chen & Chandni Akbar & Sze Ming Fu & Tejender S Rawat & Sparsh Pratik & Rajat Butola & Shih Han Chen & Albert S Lin, 2019. "Analytics-statistics mixed training and its fitness to semisupervised manufacturing," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.
    10. Tae San Kim & Jong Wook Lee & Won Kyung Lee & So Young Sohn, 2022. "Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1715-1724, August.
    11. Meir, Yuval & Tevet, Ofek & Tzach, Yarden & Hodassman, Shiri & Kanter, Ido, 2024. "Role of delay in brain dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    12. Mohanrasu, S.S. & Janani, K. & Rakkiyappan, R., 2024. "A COPRAS-based Approach to Multi-Label Feature Selection for Text Classification," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 3-23.
    13. Hamid Bekamiri & Daniel S. Hain & Roman Jurowetzki, 2021. "PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT," Papers 2103.11933, arXiv.org, revised Oct 2021.
    14. Yi-Hui Chen & Eric Jui-Lin Lu & Yu-Ting Lin & Ya-Wen Cheng, 2016. "Document overlapping clustering using formal concept analysis," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(2), pages 28-34.
    15. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    16. Han Zou & Jing Ge & Ruichao Liu & Lin He, 2023. "Feature Recognition of Regional Architecture Forms Based on Machine Learning: A Case Study of Architecture Heritage in Hubei Province, China," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    17. Josef Schwaiger & Timo Hammerl & Johannsen Florian & Susanne Leist, 2021. "UR: SMART–A tool for analyzing social media content," Information Systems and e-Business Management, Springer, vol. 19(4), pages 1275-1320, December.
    18. Verwaeren, Jan & Waegeman, Willem & De Baets, Bernard, 2012. "Learning partial ordinal class memberships with kernel-based proportional odds models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 928-942.
    19. Seyoung Park & Jaeyeon Jang & Chang Ouk Kim, 2021. "Discriminative feature learning and cluster-based defect label reconstruction for reducing uncertainty in wafer bin map labels," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 251-263, January.
    20. Huazhen Wang & Xin Liu & Bing Lv & Fan Yang & Yanzhu Hong, 2014. "Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-14, June.

    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:36:y:2025:i:1:d:10.1007_s10845-023-02242-w. 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.