• DocumentCode
    2579176
  • Title

    Automatic die inspection for post-sawing LED wafers

  • Author

    Chang, Chuan-Yu ; Li, Chun-Hsi ; Chang, Yung-Chi ; Jeng, MuDer

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Yunlin Univ. of Sci.&Technol., Douliou, Taiwan
  • fYear
    2009
  • fDate
    11-14 Oct. 2009
  • Firstpage
    1615
  • Lastpage
    1620
  • Abstract
    Wafer defect inspection is an important process that is performed before die packaging. Conventional wafer inspections are usually performed using human visual judgment. A large number of people visually inspect wafers and hand-mark the defective regions. This requires considerable personnel resources and misjudgment may be introduced due to human fatigue. In order to overcome these shortcomings, this study develops an automatic inspection system that can recognize defective LED dies. An artificial neural network is adopted in the inspection. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in the experiments. The results show that the proposed approach successfully identified the defective dies on LED wafers. Personnel costs and misjudgment due to human fatigue can be reduced using the proposed approach.
  • Keywords
    inspection; light emitting diodes; neural nets; semiconductor device manufacture; artificial neural network; automatic die inspection; automatic inspection system; defective LED dies; die packaging; human visual judgment; personnel resources; post-sawing LED wafers; semiconductor manufacturing company; wafer defect inspection; wafer inspection; Cybernetics; Fatigue; Humans; Inspection; Light emitting diodes; Manufacturing; Neural networks; Packaging; Personnel; USA Councils; Automatic inspection; Post-sawing LED inspection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
  • Conference_Location
    San Antonio, TX
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4244-2793-2
  • Electronic_ISBN
    1062-922X
  • Type

    conf

  • DOI
    10.1109/ICSMC.2009.5346751
  • Filename
    5346751