New anomaly detection in semiconductor manufacturing process using oversampling method

Seunghwan Song, Jun Geol Baek

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    Quality in the semiconductor manufacturing process, consisting of various production systems, leads to economic factors, which necessitates sophisticated abnormal detection. However, since the semiconductor manufacturing process has many sensors, there is a problem with the curse of dimensionality. It also has a high imbalance ratio, which creates a classification model that is skewed to multiple class, thus reducing the class classification performance of a minority class, which makes it difficult to detect anomalies. Therefore, this paper proposes AEWGAN (Autoencoder Wasserstein General Advertising Networks), a method for efficient anomaly detection in semiconductor manufacturing processes with high-dimensional imbalanced data. First, learn autoencoder with normal data. Abnormal data is oversampled using WGAN (Wasserstein General Additional Networks). Then, efficient anomaly detection within the potential is carried out through the previously learned autoencoder. Experiments on wafer data were applied to verify performance, and of the various methods, AEWGAN was found to have excellent performance in abnormal detection.

    Original languageEnglish
    Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
    EditorsAna Rocha, Luc Steels, Jaap van den Herik
    PublisherSciTePress
    Pages926-932
    Number of pages7
    ISBN (Electronic)9789897583957
    Publication statusPublished - 2020
    Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
    Duration: 2020 Feb 222020 Feb 24

    Publication series

    NameICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
    Volume2

    Conference

    Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020
    Country/TerritoryMalta
    CityValletta
    Period20/2/2220/2/24

    Bibliographical note

    Publisher Copyright:
    Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

    Keywords

    • Anomaly Detection
    • Autoencoder
    • Latent Space
    • Semiconductor Manufacturing Process
    • Wasserstein Generative Adversarial Networks

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software

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