Mixed Pattern Recognition Methodology on Wafer Maps with Pre-trained Convolutional Neural Networks

Yunseon Byun, Jun-Geol Baek

Abstract

In the semiconductor industry, the defect patterns on wafer bin map are related to yield degradation. Most companies control the manufacturing processes which occur to any critical defects by identifying the maps so that it is important to classify the patterns accurately. The engineers inspect the maps directly. However, it is difficult to check many wafers one by one because of the increasing demand for semiconductors. Although many studies on automatic classification have been conducted, it is still hard to classify when two or more patterns are mixed on the same map. In this study, we propose an automatic classifier that identifies whether it is a single pattern or a mixed pattern and shows what types are mixed. Convolutional neural networks are used for the classification model, and convolutional autoencoder is used for initializing the convolutional neural networks. After trained with single-type defect map data, the model is tested on single-type or mixed-type patterns. At this time, it is determined whether it is a mixed-type pattern by calculating the probability that the model assigns to each class and the threshold. The proposed method is experimented using wafer bin map data with eight defect patterns. The results show that single defect pattern maps and mixed-type defect pattern maps are identified accurately without prior knowledge. The probability-based defect pattern classifier can improve the overall classification performance. Also, it is expected to help control the root cause and management the yield.

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Paper Citation


in Harvard Style

Byun Y. and Baek J. (2020). Mixed Pattern Recognition Methodology on Wafer Maps with Pre-trained Convolutional Neural Networks.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 974-979. DOI: 10.5220/0009177909740979


in Bibtex Style

@conference{icaart20,
author={Yunseon Byun and Jun-Geol Baek},
title={Mixed Pattern Recognition Methodology on Wafer Maps with Pre-trained Convolutional Neural Networks},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={974-979},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009177909740979},
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 - Mixed Pattern Recognition Methodology on Wafer Maps with Pre-trained Convolutional Neural Networks
SN - 978-989-758-395-7
AU - Byun Y.
AU - Baek J.
PY - 2020
SP - 974
EP - 979
DO - 10.5220/0009177909740979