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Automated optical inspection system for surface mount device light emitting diodes

Author

Listed:
  • Chung-Feng Jeffrey Kuo

    (National Taiwan University of Science and Technology)

  • Tz-ying Fang

    (National Taiwan University of Science and Technology)

  • Chi-Lung Lee

    (National Taiwan University of Science and Technology)

  • Han-Cheng Wu

    (National Taiwan University of Science and Technology)

Abstract

Surface-mount device light emitting diode (SMD-LED) is characterized by small size, wide viewing angle and light weight. It becomes the main package type of LED gradually. The traditional visual inspection is likely to cause misrecognition due to personal subjectivity and different defect recognition standards. Therefore, this study develops an automatic SMD-LED defect detection system, which is characterized by non-contact inspection, defect recognition standardization and upgrading product quality. It detects the common and important defects in LED package components, including missing component, no chip, wire shift and foreign material. In this study the gray scale characteristic of histogram is used as the rapid sieving analysis indicator of missing component defect, and then the component and solder joint are positioned by using fast normalized cross-correlation, and the maximum correlation coefficient value is used as judgment indicator of no chip defect. In order to overcome the difficult identification as the weld line is subject to light rays, the improved Michelson-like contrast (MLC) enhancement is proposed, and the segmentation threshold is selected by entropy information to segment the weld line successfully. Furthermore, in order to overcome the effect of the tolerance of component size and internal electrode and unfixed weld line position resulted from lead frame process on foreign material detection result, the multiscale adaptive Fourier analysis (MAFA) is proposed in the concept of texture anomaly detection for foreign material defect detection. The result proves that the proposed method can segment the defect effectively and correctly compared with the phase-only transform (PHOT) and multiscale phase-only transform (MPHOT), and it can be used in other fields of texture anomaly detection. The overall recognition rate of this system is 98.25%, contributing to the large market demand and high component quality of LED industry.

Suggested Citation

  • Chung-Feng Jeffrey Kuo & Tz-ying Fang & Chi-Lung Lee & Han-Cheng Wu, 2019. "Automated optical inspection system for surface mount device light emitting diodes," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 641-655, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1270-6
    DOI: 10.1007/s10845-016-1270-6
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    References listed on IDEAS

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    1. Chien-Chang Hsu & Min-Sheng Chen, 2016. "Intelligent maintenance prediction system for LED wafer testing machine," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 335-342, April.
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    Cited by:

    1. 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.
    2. Zheng Xiao & Zhenan Wang & Deng Liu & Hui Wang, 2022. "A path planning algorithm for PCB surface quality automatic inspection," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1829-1841, August.

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