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Application of Machine Vision Technology in Defect Detection of High Performance Phase Noise Measurement Chips

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03 sept 2024

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Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021). Surface defect detection methods for industrial products: A review. Applied Sciences, 11(16), 7657. Search in Google Scholar

Jeon, M., Yoo, S., & Kim, S. W. (2022). A contactless PCBA defect detection method: Convolutional neural networks with thermographic images. IEEE Transactions on Components, Packaging and Manufacturing Technology, 12(3), 489-501. Search in Google Scholar

Nguyen, V. T., & Bui, H. A. (2022). A real-time defect detection in printed circuit boards applying deep learning. EUREKA: Physics and Engineering,(2), 143-153. Search in Google Scholar

Jha, S. B., & Babiceanu, R. F. (2023). Deep CNN-based visual defect detection: Survey of current literature. Computers in Industry, 148, 103911. Search in Google Scholar

Stavropoulos, P., Papacharalampopoulos, A., & Petridis, D. (2020). A vision-based system for real-time defect detection: a rubber compound part case study. Procedia CIRP, 93, 1230-1235. Search in Google Scholar

Rehman, S. U., Thang, K. F., & Lai, N. S. (2019). Automated PCB identification and defect-detection system (APIDS). International Journal of Electrical and Computer Engineering, 9(1), 297. Search in Google Scholar

Qin, J. (2023, November). Research and Application of Defect Detection Method for Electronic Wafer Based on Machine Vision. In 2023 International Conference on Artificial Intelligence and Automation Control (AIAC) (pp. 156-159). IEEE. Search in Google Scholar

Yang, L., & Liu, H. (2021, October). Research and Implementation of Defect Detection Algorithm for Chip Shell Based on VisionPro. In Proceedings of the 4th International Conference on Information Technologies and Electrical Engineering (pp. 1-5). Search in Google Scholar

Liu, Z., Ukida, H., Niel, K., & Ramuhalli, P. (2015). Industrial inspection with open eyes: Advance with machine vision technology. Integrated Imaging and Vision Techniques for Industrial Inspection: Advances and Applications, 1-37. Search in Google Scholar

Patel, D. R., Oza, A. D., & Kumar, M. (2023). Integrating intelligent machine vision techniques to advance precision manufacturing: a comprehensive survey in the context of mechatronics and beyond. International Journal on Interactive Design and Manufacturing (IJIDeM), 1-12. Search in Google Scholar

Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications—a survey. Sensors, 20(5), 1459. Search in Google Scholar

Zhao, X., Wang, Y., Li, L., & Liu, F. (2022). Precise Positioning and Defect Detection of Semiconductor Chip Based on Microvision. In Cognitive Systems and Information Processing: 6th International Conference, ICCSIP 2021, Suzhou, China, November 20–21, 2021, Revised Selected Papers 6 (pp. 451-462). Springer Singapore. Search in Google Scholar

Singh, S. A., Kumar, A. S., & Desai, K. A. (2023). Comparative assessment of common pre-trained CNNs for vision-based surface defect detection of machined components. Expert Systems with Applications, 218, 119623. Search in Google Scholar

Hou, H., & Wu, F. (2020). AUTOMATED DEFECT INSPECTION ALGORITHM FOR SEMICONDUCTOR-PACKAGED CHIPS. International Journal of Industrial Engineering, 27(5). Search in Google Scholar

Chen, S. H., & Tsai, C. C. (2021). SMD LED chips defect detection using a YOLOv3-dense model. Advanced engineering informatics, 47, 101255. Search in Google Scholar

Ming, W., Shen, F., Li, X., Zhang, Z., Du, J., Chen, Z., & Cao, Y. (2020). A comprehensive review of defect detection in 3C glass components. Measurement, 158, 107722. Search in Google Scholar

Tang, J., Huang, Z., Zhu, D., Li, H., & Zhao, L. (2022, April). Machine vision based insulator image chip drop fault recognition method. In Journal of Physics: Conference Series (Vol. 2260, No. 1, p. 012048). IOP Publishing. Search in Google Scholar

Lin, H. D., & Chen, H. L. (2018). Automated visual fault inspection of optical elements using machine vision technologies. Journal of Applied Engineering Science, 16(4), 447-453. Search in Google Scholar

Wang, S., Wang, H., Yang, F., Liu, F., & Zeng, L. (2022). Attention-based deep learning for chip-surface-defect detection. The International Journal of Advanced Manufacturing Technology, 121(3), 1957-1971. Search in Google Scholar

Chen, I. C., Hwang, R. C., & Huang, H. C. (2023). PCB defect detection based on deep learning algorithm. Processes, 11(3), 775. Search in Google Scholar

Le, H. N., Nguyen, T. V., & Debnath, N. C. (2020). A machine vision based automatic optical inspection system for detecting defects of PCBA. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (pp. 480-489). Springer International Publishing. Search in Google Scholar

Ling, Q., & Isa, N. A. M. (2023). Printed circuit board defect detection methods based on image processing, machine learning and deep learning: A survey. IEEE Access. Search in Google Scholar

Liu, Y., Guo, L., Gao, H., You, Z., Ye, Y., & Zhang, B. (2022). Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review. Mechanical Systems and Signal Processing, 164, 108068. Search in Google Scholar

Arjun, P., & Mirnalinee, T. T. (2016). Machine parts recognition and defect detection in automated assembly systems using computer vision techniques. Rev. Téc. Ing. Univ. Zulia, 39(1), 71-80. Search in Google Scholar

Lu, S., Zhang, J., Hao, F., & Jiao, L. (2022). Automatic Detection of Chip Pin Defect in Semiconductor Assembly Using Vision Measurement. Measurement Science Review, 22(5), 231-240. Search in Google Scholar

Ou, X., Chen, W., & Zhang, M. (2021, July). Design of LQFP chip pin defect detection system based on machine vision. In 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 69-76). IEEE. Search in Google Scholar

Jian, C., Gao, J., & Ao, Y. (2017). Automatic surface defect detection for mobile phone screen glass based on machine vision. Applied Soft Computing, 52, 348-358. Search in Google Scholar

Zhou, Y., Yuan, M., Zhang, J., Ding, G., & Qin, S. (2023). Review of vision-based defect detection research and its perspectives for printed circuit board. Journal of Manufacturing Systems, 70, 557-578. Search in Google Scholar

Singh, S. A., & Desai, K. A. (2023). Automated surface defect detection framework using machine vision and convolutional neural networks. Journal of Intelligent Manufacturing, 34(4), 1995-2011. Search in Google Scholar

Liu, Z., & Qu, B. (2021). Machine vision based online detection of PCB defect. Microprocessors and Microsystems, 82, 103807. Search in Google Scholar

Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the art in defect detection based on machine vision. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(2), 661-691. Search in Google Scholar

Zhou, J. (2023). Application of machine vision technology in defect detection of high-performance phase noise measurement chips. 3c Tecnología: glosas de innovación aplicadas a la pyme, 12(2), 347-362. Search in Google Scholar

Caiqiao Xiong,Yixin Pan,Jinghan Fan,Yuze Li,Jiyun Wang & Zongxiu Nie.(2024).Accurate and High-Resolution Particle Mass Measurement Using a Peak Filtering Algorithm..Analytical chemistry Search in Google Scholar

Zihan Xiong,Lan Yu,Sha An,Juanjuan Zheng,Ying Ma,Vicente Micó & Peng Gao.(2024).Automatic identification and analysis of cells using digital holographic microscopy and Sobel segmentation.Frontiers in Photonics Search in Google Scholar

Fang Liu, Chen Liang, Zhihao Guo, Weizheng Zhao, Xinyu Huang, Qihao Zhou & Feiyun Cong.(2024). Fault diagnosis of rolling bearings under varying speeds based on gray level co-occurrence matrix and DCCNN.Measurement114955-. Search in Google Scholar

Chen Xu,Yujie Zhu,Cheng Chen,Xibei Jia,Shaoshuo Li & Lei Zhang.(2024).Repose angle prediction of railway ballast based on Hopper Flow Test and PCA-Stacking ensemble learning method.Transportation Geotechnics101301-101301. Search in Google Scholar

Hairong Zhang,Yitao Hu,Xushen Li,Kun Du,Tingxiang Zeng & Canping Li.(2024).Application of support vector machines and genetic algorithms to fluid identification in Offshore Granitic subduction hill reservoirs.Geoenergy Science and Engineering213013-213013. Search in Google Scholar

Han Xu, Lu Zhang, Xuanbo Wang, Baocheng Han, Zhengyuan Luo & Bofeng Bai.(2024).Improved genetic algorithm for pipe diameter optimization of an existing large-scale district heating network. Energy131970-. Search in Google Scholar