English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54367/62174 (87%)
Visitors : 14651056      Online Users : 62
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTHU 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
    National Tsing Hua University Institutional Repository > 工學院  > 工業工程與工程管理學系 > 會議論文  >  LOGIC product yield analysis by Wafer Bin Map pattern recognition supervised neural network

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/46340

    Title: LOGIC product yield analysis by Wafer Bin Map pattern recognition supervised neural network
    Authors: Chen, F.L.;Sheng-Che Lin;Yih-Yuh Doong, K.;Young, K.L.
    教師: 陳飛龍
    Date: 2003
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: Semiconductor Manufacturing, 2003 IEEE International Symposium on, 30 Sept.-2 Oct. 2003, Pages 501 - 504
    Keywords: Wafer
    Abstract: Wafer Bin Maps (WBMs) are important for yield improvement to trace root causes. The characteristic of WBMs patterns are formed by processes, so process engineers can collect clues from the patterns and correlate them with specific processes, and this can save much time and efforts in finding the root causes. However, the existing learning algorithms have the main shortage of product dependency. For this reason, this work adopted a supervised learning methodology to develop an on-line WBMs pattern recognition system that maps WBMs into 70×70 binary images to solve this issue. Furthermore, this work also proposed a learning scheme to recognize repeating failures that are usually viewed as random pattern in the existing approaches
    URI: http://www.ieee.org/
    Appears in Collections:[工業工程與工程管理學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    2020408030002.pdf299KbAdobe PDF1966View/Open


    SFX Query


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback