Skip to main content
SpringerLink
Log in

Proposals for Development of the Prospective System for Optical Quality Control of the Assembly of Microelectronic Devices

  • DIAGNOSTICS
  • Published:
Russian Microelectronics Aims and scope Submit manuscript

Abstract

The solution of tasks of detection and classification of defects of printed circuit boards using machine learning is discussed in this article. Six classes of defects are defined. We carried out manual annotation of defects on images of printed circuit boards in Roboflow. The YOLO v.8 neural network model was used with the settings of its training parameters. To improve the accuracy of defect detection we have expanded the dataset and have conducted the experiments. The results of the automatic search for defects in printed circuit boards are presented and explained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

REFERENCES

  1. Chebotarev, S.S. and Golubev, S., Methodological approaches to the effective implementation of strategic import substitution programs, Business Strategies, 2017, vol. 19, no. 7, pp. 68–77.

    Google Scholar 

  2. Kovalevsky, Yu., Microelectronic mini-factories in the Russian defense industry–The path to import independence in the field of electronic components, Elektron.: Nauka, Tekhnol., Biznes, 2017, no. 1, pp. 60–67. https://doi.org/10.22184/1992-4178.2017.161.1.60.67

  3. Huang, W. and Wei, P., A PCB dataset for defects detection and classification, J. LaTeX Class Files, 2018, vol. 14, no. 8.

  4. Chaudhary, V., Dave, I.R., and Upla, K.P., Automatic visual inspection of printed circuit board for defect detection and classification, 2017 Int. Conf. on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, 2018, IEEE, 2018, pp. 732–737. https://doi.org/10.1109/wispnet.2017.8299858

  5. Heriansyah, R., Al-Attas, S.A.R., and Ahmad Zabidi, M.M., Neural network paradigm for classification of defects on PCB, Jurnal Teknol., 2003, vol. 39, no. 1, pp. 87–103. https://doi.org/10.11113/jt.v39.465

    Article  Google Scholar 

  6. Pramerdorfer, C. and Kampel, M., A dataset for computer-vision-based PCB analysis, 2015 14th IAPR Int. Conf. on Machine Vision Applications (MVA), Tokyo, 2015, IEEE, 2015, pp. 378–381. https://doi.org/10.1109/mva.2015.7153209

  7. Wu, W.-Ye., Wang, M.-J.J., and Liu, Ch.-M., Automated inspection of printed circuit boards through machine vision, Comput. Ind., 1996, vol. 28, no. 2, pp. 103–111. https://doi.org/10.1016/0166-3615(95)00063-1

    Article  Google Scholar 

  8. Zheng-Ming, L.I., Hong, L.I., and Jun, S., Detection of PCB-based on digital image processing, Instrum. Technique Sensor, 2012, vol. 61, no. 8, pp. 87–89.

    Google Scholar 

  9. Zhou, S., Chen, Yo., Zhang, D., Xie, J., and Zhou, Yu., Classification of surface defects on steel sheet using convolutional neural networks, Mater. Tehnol., 2017, vol. 51, no. 1, pp. 123–131. https://doi.org/10.17222/mit.2015.335

    Article  Google Scholar 

  10. Kovalevsky, Yu., When the processes are tuned for quality and reliability. Visit to production facility of macro EMS Ltd, Elektron.: Nauka, Tekhnol., Biznes, 2020, no. 5, pp. 34–42. https://doi.org/10.22184/1992-4178.2020.196.5.34.42

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. Joint-stock company “Research Institute “Submicron, 124498, Zelenograd, Russia

    K. S. Balizh, P. M. Eremeev & E. A. Simakhina

Authors
  1. K. S. Balizh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. M. Eremeev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. A. Simakhina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to K. S. Balizh or E. A. Simakhina.

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balizh, K.S., Eremeev, P.M. & Simakhina, E.A. Proposals for Development of the Prospective System for Optical Quality Control of the Assembly of Microelectronic Devices. Russ Microelectron 52 (Suppl 1), S246–S250 (2023). https://doi.org/10.1134/S1063739723600280

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1063739723600280

Keywords:

Search

Navigation