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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/83938

    Title: Defective Wafer Detection Using Sensed Numerical Features
    Authors: 林靖宇;Lin, Ching-Yu
    Contributors: 資訊工程學系在職專班
    Keywords: GRU神經網絡;XGBoost;深度學習;缺陷晶圓檢測;GRU neural networks;XGBoost;Deep learning;Defective wafer detection
    Date: 2020-07-30
    Issue Date: 2020-09-02 17:43:21 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 半導體製造的基本過程之一是切片,這意味著將晶棒切成許多晶片。
    我們在此系統中應用兩種不同模型-GRU 神經網絡和XGBoost。此兩種模型
    GRU 神經網絡的預測準確度和模型訓練時間均優於XGBoost;One of the fundamental processes in semiconductor manufacturing is slicing,
    which means cutting an ingot into many wafers. During the slicing process, it
    is possible to produce defective wafers. Unfortunately, the inspection to identify
    defective wafers is time-consuming and di cult. To solve this problem, we build a
    system, which uses sensors to collect features (e.g., temperature, thickness, pattern
    on wafer surface, etc.) during the slicing process to detect if the wafers are defective
    in the manufacturing process. Two di erent models, the GRU neural network and
    XGBoost, are implemented in the proposed system. After ne-tuning both models,
    experimental results based on real dataset indicate that the GRU neural network
    outperforms XGBoost for wafer defective detection in both the prediction accuracy
    and model training time.
    Appears in Collections:[資訊工程學系碩士在職專班 ] 博碩士論文

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