Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chang, CY., Wang, HJ., Lin, SY. (2006). Simulation Studies of Two-Layer Hopfield Neural Networks for Automatic Wafer Defect Inspection. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_119
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DOI: https://doi.org/10.1007/11779568_119
Publisher Name: Springer, Berlin, Heidelberg
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