Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects

Shing Chiang Tan, Junzo Watada, Zuwairie Ibrahim, Marzuki Khalid

    研究成果: Article

    38 引用 (Scopus)

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    Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

    元の言語English
    記事番号6847172
    ページ(範囲)933-950
    ページ数18
    ジャーナルIEEE Transactions on Neural Networks and Learning Systems
    26
    発行部数5
    DOI
    出版物ステータスPublished - 2015 5 1

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Science Applications
    • Software

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