Journal of Intelligent Manufacturing

, Volume 22, Issue 6, pp 953–964 | Cite as

Wafer defect inspection by neural analysis of region features

  • Chuan-Yu Chang
  • Chun-Hsi Li
  • Yung-Chi Chang
  • MuDer Jeng


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.


RBF neural network Wafer defect detection 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Chuan-Yu Chang
    • 1
  • Chun-Hsi Li
    • 2
  • Yung-Chi Chang
    • 1
  • MuDer Jeng
    • 2
  1. 1.Department of Computer Science and Information EngineeringNational Yunlin University of Science & TechnologyDouliuTaiwan, Republic of China
  2. 2.Department of Electrical EngineeringNational Taiwan Ocean UniversityKeelungTaiwan, Republic of China

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