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Automatic optical inspection system for IC molding surface

Author

Listed:
  • Ssu-Han Chen

    (Ming Chi University of Technology)

  • Der-Baau Perng

    (Asia University)

Abstract

Success or failure of an IC product hinges on the quality of molding process which protects chips from the harm done by external force and moisture. Defects such as cracks, dilapidations or voids may be embedding on the molding surface while a chip was being molded. Human inspection often neglects a very tiny crack or a low-contrast void. Hence an automatic optical inspection system for the integrated circuit (IC) molding surface cannot be over emphasized. The proposed system is composed of a charged coupled device, a coaxial light, a back light and a motion control unit. Based on the characteristics of statistical textures of the molding surface, a series of digital image processing is carried out, including normalization, shrinking, segmenting and Fourier based image restoration and defect identification. Training of the parameter associated with defect inspection algorithm is also discussed. Results of the experiment suggest that the inspection system works effectively with high accuracy rate of 94.2 %, contributing to the inspection quality of IC molding surface.

Suggested Citation

  • Ssu-Han Chen & Der-Baau Perng, 2016. "Automatic optical inspection system for IC molding surface," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 915-926, October.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:5:d:10.1007_s10845-014-0924-5
    DOI: 10.1007/s10845-014-0924-5
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    Cited by:

    1. Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    2. Chih-Kai Cheng & Hung-Yin Tsai, 2022. "Enhanced detection of diverse defects by developing lighting strategies using multiple light sources based on reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2357-2369, December.
    3. Mohamed Ben Gharsallah & Ezzedine Ben Braiek, 2021. "Computer aided manufacturing method for surface silicon steel inspection based on an efficient anisotropic diffusion algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1025-1041, April.
    4. Keyur D. Joshi & Vedang Chauhan & Brian Surgenor, 2020. "A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 103-125, January.

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