Details

Autor(en) / Beteiligte

• Chuan-Yu Chang
• Si-Yan Lin
• MuDer Jeng

Titel

Using a two-layer competitive Hopfield neural network for semiconductor wafer defect detection

Ist Teil von

• IEEE International Conference on Automation Science and Engineering, 2005, 2005, p.301-306

Ort / Verlag

IEEE

Erscheinungsjahr

2005

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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 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.