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Intelligent maintenance prediction system for LED wafer testing machine

Author

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  • Chien-Chang Hsu

    (Fu-Jen Catholic University)

  • Min-Sheng Chen

    (Fu-Jen Catholic University)

Abstract

Achieving high quality production of light-emitting diode (LED) wafers requires robust monitoring and the use of a stable test machine. In many factories, production continues 24 h a day. Stopping the manufacturing process at a factory is often difficult. Therefore, reducing inspection time and ensuring the stability of test machines are important. Traditionally, LED wafer factories examine their test machines during periodic maintenance. Standard lamp adjustments are performed to ensure their accuracy. This process interrupts the manufacturing process and requires extra manpower. It reduces productivity and increases production cost. Additionally, the accurate assessment of the aging of the components of the machine requires an experienced engineer. Correctly timing the maintenance and replacing the aging components of the LED wafer test machine are important. This work performed feature extraction to identify the working attributes of an LED wafer test machine. The intelligent maintenance prediction system then uses the radial basis function neural network and variability of the working attributes to predict the maintenance times and aging of the LED wafer test machines. Experimental results reveal that the accuracy of proposed system in predicting maintenance times exceeds 98 %.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-013-0866-3
    DOI: 10.1007/s10845-013-0866-3
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    References listed on IDEAS

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    1. Paula Andrea Potes Ruiz & Bernard Kamsu-Foguem & Daniel Noyes, 2013. "Knowledge reuse integrating the collaboration from experts in industrial maintenance management," Post-Print hal-00861829, HAL.
    2. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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    Cited by:

    1. Sangho Lee & Youngdoo Son, 2021. "Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-21, June.
    2. Diyi Zhou & Shihua Gong & Ziyue Wang & Delong Li & Huaiqing Lu, 2021. "Error analysis based on error transfer theory and compensation strategy for LED chip visual localization systems," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1345-1359, June.
    3. Seokho Kang, 2020. "Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 319-326, February.
    4. 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.

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