Simulation Studies of Two-Layer Hopfield Neural Networks for Automatic Wafer Defect Inspection

  • Chuan-Yu Chang
  • Hung-Jen Wang
  • Si-Yan Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


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.


Hopfield Neural Network Defective Region Visual Judgment Defect Inspection Image Edge Detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Su, C.-T., Yang, T., Ke, C.-M.: Neural-network approach for Semiconductor wafer post-sawing inspection. IEEE Trans. Semi. Man. 15(2), 260–266 (2002)CrossRefGoogle Scholar
  2. 2.
    Tobin Jr., K.W., Karnowski, T.P., Lakhani, F.: Integrated applications of inspection data in the semiconductor manufacturing environment. SPIE, Metrology-based Control for Micro-Manufacturing 4275, 31–40 (2001)CrossRefGoogle Scholar
  3. 3.
    Mital, D.P., Teoh, E.K.: Computer based wafer inspection system. In: Proceeding of International Conference on Industrial Electronics, Control and Instrumentation, vol. 3, pp. 2497–2503 (1991)Google Scholar
  4. 4.
    Zang, J.M., Lin, R.M., Wang, M.J.: The development of an automatic post-sawing inspection system using computer vision techniques. Computers in Industry 30, 51–60 (1999)CrossRefGoogle Scholar
  5. 5.
    Chang, C.-Y., Chung, P.-C.: Two layer competitive based Hopfield neural network for medical image edge detection. Optical Engineering 39(3), 695–703 (2000)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Cheriet, M., Said, J.N., Suen, C.Y.: A recursive thresholding technique for image segmentation. IEEE Trans. Image Processing 7, 918–921 (1998)CrossRefGoogle Scholar
  7. 7.
    Lin, J.S., Cheng, K.S., Mao, C.W.: The application of competitive Hopfield neural network to medical image segmentation. IEEE Trans. Med. Imag. 15, 560–567 (1996)CrossRefGoogle Scholar
  8. 8.
    Penedo, M.G., Carreria, M.J., Mosquera, A., Cabello, D.: Computer-Aided Diagnosis: A Neural-Network-Based Approach to Lung Nodule Detection. IEEE Trans. Med. Imag. 17, 872–880 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chuan-Yu Chang
    • 1
  • Hung-Jen Wang
    • 1
  • Si-Yan Lin
    • 1
  1. 1.Department of Electronic EngineeringNational Yunlin University of Science and TechnologyTaiwan

Personalised recommendations