Mobile QR Code QR CODE

The Journal of Semiconductor Technology and Science (JSTS) is an international, peer-reviewed, and open-access journal that is published bimonthly.
- Scope: semiconductor processes, devices, circuits, and MEMS.
- Editor-in-Chief: Prof. Woo Young Choi (ECE, Seoul National University)
- Indexed within Science Citation Index Expanded (SCIE), SCOPUS, Korea Citation Index (KCI), and other databases.

Journal Search

  1. Home
  2. Archive
  3. 2025-06 (Vol.25 No.03)
  4. 10.5573/JSTS.2025.25.3.245
XML PDF INFO REF

References ▼

[1]

J. Serey, M. Alfaro, G. Fuertes, M. Varhas, C. Durán, R. Ternero, R. Rivera, and J. Sabattin, “Pattern recognition and deep learning technologies, enablers of Industry 4.0, and their role in engineering research,” Symmetry, vol. 15, no. 2, 535, 2023. [CrossRef]

[2]

J. Wang, Y. Ma, L. Zhang, R. X. Gao, and D. Wu, “Deep learning for smart manufacturing: Methods and applications,” Journal of Manufacturing Systems, vol. 48, pp. 144- 156, 2018. [CrossRef]

[3]

S. Sundaram and A. Zeid, “Artificial intelligence-based smart quality inspection for manufacturing,” Micromachining, vol. 14, no. 2, 570, 2023. [CrossRef]

[4]

E. Baran and T. K. Polat, “Classification of Industry 4.0 for total quality management: A review,” Sustainability, vol. 14, no. 6, 3329, 2022. [CrossRef]

[5]

T.P. Nguyen, S. Choi, S.J. Park, and J. Yoon, “Inspecting method for defective casting products with convolutional neural network (CNN),” International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 8, pp. 583-594, 2021. [CrossRef]

[6]

F. Pernkopf and P. O’Leary, “Visual inspection of machined metallic high-precision surfaces,” EURASIP Journal on Advances in Signal Processing, vol. 2002, pp. 1-12, 2002. [CrossRef]

[7]

X. Jiang, P. Scott, and D. Whitehouse, “Wavelets and their applications for surface metrology,” CIRP Annals, vol. 57, no. 1, pp. 555-558, 2008. [CrossRef]

[8]

D. H. Harris, “The nature of industrial inspection,” Human Factors, vol. 11, no. 2, pp. 139-148, 1969. [CrossRef]

[9]

J. E. See, “Visual inspection reliability for precision manufactured parts,” Human Factors, vol. 57, no. 8, pp. 1427- 1442, 2015. [CrossRef]

[10]

D. Weimer, B. Scholz-Reiter, and M. Shpitalni, “Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection,” CIRP Annals, vol. 65, no. 1, pp. 417-420, 2016. [CrossRef]

[11]

F. Riaz, K. Kamal, T. Zafar, and R. Qayyum, “An inspection approach for casting defects detection using image segmentation,” Proc. of 2017 International Conference on Mechanical, System and Control Engineering (ICMSC), pp. 101-105, 2017. [CrossRef]

[12]

M. Ferguson, R. Ak, Y.-T. T. Lee, and K. H. Law, “Automatic localization of casting defects with convolutional neural networks,” Proc. of 2017 IEEE International Conference on Big Data (Big Data), pp. 1726-1735, December 2017. [CrossRef]

[13]

M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science, vol. 349, no. 6245, pp. 255-260, 2015. [CrossRef]

[14]

H. Xie and ZWu, “A robust fabric defect detection method based on improved RefineDet,” Sensors, vol. 15, no. 15, 2020. [CrossRef]

[15]

P. Murray, E. Yakushina, S. Marshall, and W. Lon, “Automated microstructural analysis of titanium alloys using digital image processing,” IOP Conference Series: Materials Science and Engineering, vol. 179, 012011, 2017. [CrossRef]

[16]

M. M. Islam, A. A. Mintoo, and A. S. M. Saimon, “Enhancing textile quality control with IoT sensors: A case study of automated defect detection,” Global Mainstream Journal, vol. 1, no. 1, pp. 19-30, 2024. [CrossRef]

[17]

J. Breitenbach, I. Eckert, V. Mahal, H. Baumgartl, and R. Buettner, “Automated defect detection of screws in the manufacturing industry using convolutional neural networks,” Proc. of the 55th Hawaii International Conference on System Sciences, 2022. [CrossRef]

[18]

L. Song, X. Li, Y. Yang, X. Zhu, Q. Guo, and H. Yang, “Detection of micro-defects on metal screw surfaces based on deep convolutional neural networks,” Sensors, vol. 18, no. 11, 2018. [CrossRef]

[19]

Z. Liu, B. Tian, X. Li, C. Li, and Y. Dong, “Saliencybased fabric defect detection network with feature pyramid learning and refinement,” Proc. of Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), vol. 12705, 127050N, 2023. [CrossRef]

[20]

S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, “Classification of surface defects on steel sheet using convolutional neural networks,” Materiali in Tehnologije/Materials and Technology, vol. 51, no. 1, pp. 123-131, 2017. [CrossRef]

[21]

A. S˛eker, K. A. Peker, A. G. Yüksek, and E. Delibas˛, “Fabric defect detection using deep learning,” Proc. of 2016 24th Signal Processing and Communication Application Conference (SIU), IEEE, pp. 1437-1440, 2016. [CrossRef]

[22]

S.-H. Huang and Y.-C Pan, “Automated visual inspection in the semiconductor industry: A survey,” Computers in Industry, vol. 66, pp. 1-10, 2015. [CrossRef]

[23]

Y. Qiao and L. Xing, “Automatic classification method for Oracle images based on deep learning,” IEIE Transactions on Smart Processing and Computing, vol. 12, no. 2, pp. 87-96, April 2023. [CrossRef]

[24]

S. P. Yadav, “Vision-based detection, tracking, and classification of vehicles,” IEIE Transactions on Smart Processing and Computing, vol. 9, no. 6, pp. 427-434, December 2020. [CrossRef]

[25]

M. N. Nguyen and T. Nguyen, “Deep learning approaches to human gait pattern classification based on MEMS sensors,” IEIE Transactions on Smart Processing and Computing, vol. 9, no. 4, pp. 184-292, August 2020. [CrossRef]

[26]

J. He, L. Li, J. Xu, and C. Zheng, “ReLU deep neural networks and linear finite elements,” Journal of Computational Mathematics, vol. 38, no. 3, pp. 502-527, July 2018. [CrossRef]

[27]

S. Karen and Z. Andrew, “Very deep convolutional networks roe large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. [CrossRef]

[28]

K. He, X. Zhang, S Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016. [CrossRef]

[29]

A. Dosovitskiy, “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020. [CrossRef]

[30]

Y. F. Yang and M. Sun, “Semiconductor defect detection by hybrid classical-quantum deep learning,” Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2323-2332, 2022. [CrossRef]

[31]

D. Ujalambkar, C. Kulkarni, V. Navale, and N. P. Sable, “Industrial product surface defect detection using CNN: A deep learning approach,” Panamerican Mathematical Journal, vol. 34, no. 3, 2024 [CrossRef]

[32]

S. Arikan, K. Varanasi, and D. Stricker, “Surface defect classification in real-time using convolutional neural networks,” arXiv preprint arXiv:1904.04671, 2019. [CrossRef]

[33]

B. Devika and N. George, “Convolutional neural network for semiconductor wafer defect detection,” Proc. of 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-6, 2019. [CrossRef]

[34]

J. Yang, S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang, “Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges,” Materials, vol. 13, no. 24, 5755, 2020. [CrossRef]