New Anomaly Detection in Semiconductor Manufacturing Process using Oversampling Method

Seunghwan Song, Jun-Geol Baek

2020

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.

Paper Citation

in Harvard Style

Song S. and Baek J. (2020). New Anomaly Detection in Semiconductor Manufacturing Process using Oversampling Method. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 926-932. DOI: 10.5220/0009170709260932

in Bibtex Style

@conference{icaart20,
author={Seunghwan Song and Jun-Geol Baek},
title={New Anomaly Detection in Semiconductor Manufacturing Process using Oversampling Method},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={926-932},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009170709260932},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - New Anomaly Detection in Semiconductor Manufacturing Process using Oversampling Method
SN - 978-989-758-395-7
AU - Song S.
AU - Baek J.
PY - 2020
SP - 926
EP - 932
DO - 10.5220/0009170709260932