English  |  正體中文  |  简体中文  |  Items with full text/Total items : 69561/69561 (100%)
Visitors : 23025796      Online Users : 215
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    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。
    在切片過程中,可能會產生有缺陷的晶圓。
    不幸的是,識別缺陷晶片的檢查既浪費時間又檢查困難。為了解決這個問
    題,我們建立了一個系統,該系統在切片及表面檢查過程中會使用傳感器
    收集晶圓特性(例如溫度,厚度,晶片表面上的圖案等)以檢測晶片在製
    造過程中是否有缺陷。
    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
    經過微調後,根據實際數據分析的實驗結果顯示在晶圓的缺陷檢測方面,
    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.
    ii
    Appears in Collections:[資訊工程學系碩士在職專班 ] 博碩士論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML13View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明