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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/33586

Title: Integrated Two Hopfield Neural Networks for Automatic LED Defect Inspection
Authors: Chuan-Yu Chang;Chuan-Wang Chang;Chuan-Wang Chang;Mu-Der Jeng
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: Hopfield Neural networks;LED;defect inspection
Date: 2008-02-01
Issue Date: 2013-04-12T05:55:24Z
Publisher: 中國機械工程學刊
Abstract: abstract:The aim of the wafer defect inspection is to detect defective dies and discard them. The defective dies were usually identified through visual judgment with the aid of a scanning electron microscope. Dozens of engineers visually check wafers and hand-mark the defective regions leading to a significant amount of personnel cost. In this paper, a complete solution which consists of two Hopfield neural networks is proposed to detect the defective dies of wafer image. The experimental results show the proposed method successfully identifies the defective dies on LED wafers images with good performances.
Relation: 29(1), pp.45-51
URI: http://ntour.ntou.edu.tw/handle/987654321/33586
Appears in Collections:[電機工程學系] 期刊論文

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