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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51103

Title: Wafer Defect Inspection by Neural Analysis of Region Features
Authors: Chuan-Yu Chang
Chun-Hsi Li
Yung-Chi Chang
MuDer Jeng
Contributors: 國立臺灣海洋大學電機工程學系
Keywords: RBF neural network
Wafer defect detection
Date: 2009-12
Issue Date: 2018-11-13
Publisher: Journal of Intelligent Manufacturing
Abstract: 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.
Relation: 22(6) pp.953-964
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51103
Appears in Collections:[電機工程學系] 期刊論文

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