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.
Original language | English |
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Title of host publication | ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence |
Editors | Ana Rocha, Luc Steels, Jaap van den Herik |
Publisher | SciTePress |
Pages | 974-979 |
Number of pages | 6 |
ISBN (Electronic) | 9789897583957 |
Publication status | Published - 2020 |
Event | 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta Duration: 2020 Feb 22 → 2020 Feb 24 |
Publication series
Name | ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence |
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Volume | 2 |
Conference
Conference | 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 |
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Country/Territory | Malta |
City | Valletta |
Period | 20/2/22 → 20/2/24 |
Bibliographical note
Publisher Copyright:Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
Keywords
- Classification
- Convolutional Neural Networks
- Deep Learning
- Smart Manufacturing
ASJC Scopus subject areas
- Artificial Intelligence
- Software