Simulation Studies of Two-Layer Hopfield Neural Networks for Automatic Wafer Defect Inspection
The occurrence of defect on a wafer may result in losing the yield ratio. The defective regions were usually identified through visual judgment with the aid of a scanning electron microscope. Dozens of people visually check wafers and hand-mark their defective regions leading to a significant amount of personnel cost. In addition, potential misjudgment may introduce 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 to detect the defective regions of wafer image. The CHWDNN extends the one-layer 2D Hopfield neural network at the original image plane to a two-layer 3D Hopfield neural network with defect detection to be implemented on its third dimension. With the extended 3D 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.
KeywordsHopfield Neural Network Defective Region Visual Judgment Defect Inspection Image Edge Detection
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