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Natural language processing (NLP) and association rules (AR)-based knowledge extraction for intelligent fault analysis: a case study in semiconductor industry

Author

Listed:
  • Zhiqiang Wang

    (Research Center)

  • Kenneth Ezukwoke

    (Mines Saint-Étienne, Univ. Clermont Auvergne, CNRS UMR 6158 LIMOS
    Henri FAYOL Institute)

  • Anis Hoayek

    (Mines Saint-Étienne, Univ. Clermont Auvergne, CNRS UMR 6158 LIMOS
    Henri FAYOL Institute)

  • Mireille Batton-Hubert

    (Mines Saint-Étienne, Univ. Clermont Auvergne, CNRS UMR 6158 LIMOS
    Henri FAYOL Institute)

  • Xavier Boucher

    (Mines Saint-Étienne, Univ. Clermont Auvergne, CNRS UMR 6158 LIMOS
    Center for Biomedical and Healthcare Engineering)

Abstract

Fault analysis (FA) is the process of collecting and analyzing data to determine the cause of a failure. It plays an important role in ensuring the quality in manufacturing process. Traditional FA techniques are time-consuming and labor-intensive, relying heavily on human expertise and the availability of failure inspection equipment. In semiconductor industry, a large amount of FA reports are generated by experts to record the fault descriptions, fault analysis path and fault root causes. With the development of Artificial Intelligence, it is possible to automate the industrial FA process while extracting expert knowledge from the vast FA report data. The goal of this research is to develop a complete expert knowledge extraction pipeline for FA in semiconductor industry based on advanced Natural Language Processing and Machine Learning. Our research aims at automatically predicting the fault root cause based on the fault descriptions. First, the text data from the FA reports are transformed into numerical data using Sentence Transformer embedding. The numerical data are converted into latent spaces using Generalized-Controllable Variational AutoEncoder. Then, the latent spaces are classified by Gaussian Mixture Model. Finally, Association Rules are applied to establish the relationship between the labels in the latent space of the fault descriptions and that of the fault root cause. The proposed algorithm has been evaluated with real data of semiconductor industry collected over three years. The average correctness of the predicted label achieves 97.8%. The method can effectively reduce the time of failure identification and the cost during the inspection stage.

Suggested Citation

  • Zhiqiang Wang & Kenneth Ezukwoke & Anis Hoayek & Mireille Batton-Hubert & Xavier Boucher, 2025. "Natural language processing (NLP) and association rules (AR)-based knowledge extraction for intelligent fault analysis: a case study in semiconductor industry," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 357-372, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02245-7
    DOI: 10.1007/s10845-023-02245-7
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    References listed on IDEAS

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    1. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    2. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
    3. Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
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