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- W1642077654 abstract "The occurrence of defect on a wafer may result in losing the yield ratio. The defective regions are usually identified through visual judgment with the aid of a scanning electron microscope and many people visually check wafers and hand-mark their defective regions leading to a significant amount of personnel cost. In addition, potential misjudgment may be introduced due to human fatigue. In this paper, a two-layer Hopfield neural network called the competitive Hopfield wafer-defect detection neural network (CHWDNN) is proposed for detecting the defective regions of wafer image. The CHWDNN extends the one-layer 2-D Hopfield neural network at the original image plane to a two-layer 3-D Hopfield neural network with defect detection to be implemented on its third dimension. With the extended 3-D architecture, the network is capable of incorporating a pixel's spatial information into a pixel-classifying procedure. The experimental results show the CHWDNN successfully identifies the defective regions on wafers images with good performances." @default.
- W1642077654 created "2016-06-24" @default.
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- W1642077654 date "2005-07-06" @default.
- W1642077654 modified "2023-09-26" @default.
- W1642077654 title "Two-layer competitive Hopfield neural network for wafer defect detection" @default.
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- W1642077654 doi "https://doi.org/10.1109/icnsc.2005.1461344" @default.
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