Mixed pattern recognition methodology on wafer maps with pre-trained convolutional neural networks

Yunseon Byun, Jun Geol Baek

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

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 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
Pages974-979
Number of pages6
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

  • Classification
  • Convolutional Neural Networks
  • Deep Learning
  • Smart Manufacturing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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