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Automated defect inspection of LED chip using deep convolutional neural network

Author

Listed:
  • Hui Lin

    (HUST)

  • Bin Li

    (HUST)

  • Xinggang Wang

    (HUST)

  • Yufeng Shu

    (HUST)

  • Shuanglong Niu

    (HUST)

Abstract

Defect inspection is a vital part of the production process to control the quality of LED chip. On the one hand, traditional methods are time-consuming, which rely on models badly and require rich operation experience. On the other hand, defect localization cannot be achieved by using traditional networks. To solve these problems, we achieve the application of convolutional neural network (CNN) for LED chip defect inspection. Built in the CNN, a class activation mapping technique is proposed to localize defect regions without using region-level human annotations. Further, LED chip datasets are collected for training the CNN. It is worth to emphasize that the chip defect classification and localization tasks are completed in a single CNN which is very fast and convenient. The proposed CNN based defect inspector named LEDNet achieves impressively high performance on the inspection of LED chip defects (line blemishes and scratch marks) with an inaccuracy of 5.04%, localizing exact defect regions as well.

Suggested Citation

  • Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1415-x
    DOI: 10.1007/s10845-018-1415-x
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    Citations

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    Cited by:

    1. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    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. Bikash Koli Dey & Hyesung Seok, 2024. "Intelligent inventory management with autonomation and service strategy," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 307-330, January.
    4. Omid Davtalab & Ali Kazemian & Xiao Yuan & Behrokh Khoshnevis, 2022. "Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 771-784, March.
    5. Zhenxing Cheng & Hu Wang & Gui-Rong Liu, 2021. "Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1009-1023, April.
    6. Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.
    7. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    8. Feng Huang & Ben-wu Wang & Qi-peng Li & Jun Zou, 2023. "Texture surface defect detection of plastic relays with an enhanced feature pyramid network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1409-1425, March.
    9. Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
    10. Aidong Chen & Xiang Li & Hongyuan Jing & Chen Hong & Minghai Li, 2023. "Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism," Energies, MDPI, vol. 16(4), pages 1-15, February.
    11. Yuwei Mao & Hui Lin & Christina Xuan Yu & Roger Frye & Darren Beckett & Kevin Anderson & Lars Jacquemetton & Fred Carter & Zhangyuan Gao & Wei-keng Liao & Alok N. Choudhary & Kornel Ehmann & Ankit Agr, 2023. "A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 315-329, January.
    12. Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
    13. Chengjun Xu & Guobin Zhu, 2021. "Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 237-249, January.
    14. Nhat-To Huynh & Duong-Dong Ho & Hong-Nguyen Nguyen, 2023. "An Approach for Designing an Optimal CNN Model Based on Auto-Tuning GA with 2D Chromosome for Defect Detection and Classification," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    15. Ruizhen Liu & Zhiyi Sun & Anhong Wang & Kai Yang & Yin Wang & Qianlai Sun, 2020. "Real-time defect detection network for polarizer based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1813-1823, December.

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