New anomaly detection in semiconductor manufacturing process using oversampling method

Seunghwan Song, Jun Geol Baek

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

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 Jan 1
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
CountryMalta
CityValletta
Period20/2/2220/2/24

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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