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

    7 Citations (Scopus)

    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
    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
    Country/TerritoryMalta
    CityValletta
    Period20/2/2220/2/24

    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

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