TY - GEN
T1 - Mixed pattern recognition methodology on wafer maps with pre-trained convolutional neural networks
AU - Byun, Yunseon
AU - Baek, Jun Geol
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C2005949).
Funding Information:
This work was also supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University) and by the Samsung Electronics Co., Ltd.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Classification
KW - Convolutional Neural Networks
KW - Deep Learning
KW - Smart Manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85083107766&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083107766&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85083107766
T3 - ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
SP - 974
EP - 979
BT - ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
A2 - Rocha, Ana
A2 - Steels, Luc
A2 - van den Herik, Jaap
PB - SciTePress
T2 - 12th International Conference on Agents and Artificial Intelligence, ICAART 2020
Y2 - 22 February 2020 through 24 February 2020
ER -