SpringerLink
Log in

A Survey on Automatic Inspection for Printed Circuit Board Analysis

  • Conference paper
  • First Online:
Proceedings of the 7th Brazilian Technology Symposium (BTSym’21) (BTSym 2021)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 295))

Included in the following conference series:

Abstract

The complexity of electronic circuits is increasing due to novel products such as smartphones, smart TVs, smartwatches, drones, and robots. Furthermore, the competition between the electronic companies is fiercer than ever. In this sense, product quality is a key point in the company’s business plan. Being the Printed Circuit Board (PCB) the main and most sensitive part of an electronic product, ensuring its quality is fundamental to the production process. Visual Inspection is one of the most important tools to ensure PCB quality in a manufacturing line. Considering the advances in computing systems, the Automated Optical Inspection (AOI) machines are replacing human operators in the visual inspection process. However, AOI machines usually still need highly trained operators, with knowledge of image processing, so there still has room for more automated processes using Artificial Intelligence, particularly considering the advances with Neural Networks. This is the subject of this paper, where it is presented a survey about the traditional techniques and neural networks techniques able to identify components and anomalies in various PCB types. At the end of the paper, the works are compared highlighting the pros and cons of each methodology when applied to a real production line.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 192.59
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 246.09
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 246.09
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Li, J., Gu, J., Huang, Z., Wen, J.: Application research of improved YOLO V3 algorithm in PCB electronic component detection. Appl. Sci. 9(18), 3750 (2017). https://doi.org/10.3390/app9183750

    Article  Google Scholar 

  2. Anitha, D.B., Rao, M.: A survey on defect detection in bare PCB and assembled PCB using image processing techniques. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 39–43 (2017). https://doi.org/10.1109/WiSPNET.2017.8299715

  3. Yuk, E.H., Park, S.H., Park, C.S., Baek, J.G.: Feature-learning-based printed circuit board inspection via speeded-up robust features and random forest. Appl. Sci. 8(6), 932 (2018). https://doi.org/10.3390/app8060932

    Article  Google Scholar 

  4. He, Q.: Defect detection of PCB based on Bayes feature fusion. J. Eng. 16, 1741–1745 (2018). https://doi.org/10.1049/joe.2018.8270

    Article  Google Scholar 

  5. Tsai, D., Huang, C.: Defect detection in electronic surfaces using template-based fourier image reconstruction. IEEE Trans. Compon. Packag. Manuf. Technol. 9(1), 163–172 (2019). https://doi.org/10.1109/TCPMT.2018.2873744

    Article  Google Scholar 

  6. Volkay, I., Mujeeb, A., Wenting, D., Marius, E., Alexei, S.: Detection defect in printed circuit boards using unsupervised feature extraction upon transfer learning. In: 2019 International Conference on Cyberworlds (CW), pp. 101–108 (2019). https://doi.org/10.1109/CW.2019.00025

  7. Zhu, J., Wu, A., Liu, X.: Printed circuit board defect visual detection based on wavelet denoising. IOP Conf. Ser. Mater. Sci. Eng. 392, 062055 (2018). https://doi.org/10.1088/1757-899X/392/6/062055

    Article  Google Scholar 

  8. Li, D., Li, C., Chen, C., Zhao, Z.: Semantic segmentation of a printed circuit board for component recognition based on depth images. Sensors 20(18), 5318 (2020). https://doi.org/10.3390/s20185318

    Article  Google Scholar 

  9. Richter, J., Streitferdt, D., Rosova, W.: On the development of intelligent optical inspections. In: 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1–6 (2017). https://doi.org/10.13140/2.1.4116.7044

  10. Mello, A.R., Stemmer, M.R.: Inspecting surface mounted devices using k nearest neighbor and Multilayer Perceptron. In: 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), pp. 950–955 (2015). https://doi.org/10.1109/ISIE.2015.7281599

  11. Chaudhary, V., Dave, I.R., Upla, K.P.: Automatic visual inspection of printed circuit board for defect detection and classification. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 732–737 (2017). https://doi.org/10.1109/WiSPNET.2017.8299858

  12. Shi, W., Zhang, L., Li, Y., Liu, H.: (2020) Adversarial semi-supervised learning method for printed circuit board unknown defect detection. J. Eng. 13, 505–510 (2020). https://doi.org/10.1049/joe.2019.1181

    Article  Google Scholar 

  13. Shi, W., Lu, Z., Wu, W., Liu, H.: Single-shot detector with enriched semantics for PCB tiny defect detection. J. Eng. 13, 366–372 (2020). https://doi.org/10.1049/joe.2019.1180

    Article  Google Scholar 

  14. Zhang, L.: Convolutional neural network-based multi-label classification of PCB defects. J. Eng. 2018(16), 1612–1616 (2018). https://doi.org/10.1049/joe.2018.8279

    Article  Google Scholar 

  15. De Oliveira, T.J.M., Wehrmeister, M.A., Nassu, B.T.: Detecting modifications in printed circuit boards from fuel pump controllers. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 87–94, (2017). https://doi.org/10.1109/SIBGRAPI.2017.18

  16. Adibhatla, V.A., Chih, H.C., Hsu, C.C., Cheng, J., Abbod, M.F., Shied, J.S.: Defect detection in printed circuit boards using you-only-look-once convolutional neural networks. Electronics 9(9), 1547 (2020). https://doi.org/10.3390/electronics9091547

    Article  Google Scholar 

  17. Mallaiyan Sathiaseelan, M.A., Paradis, O.P., Taheri, S., Asadizanjani, N.: Why is deep learning challenging for printed circuit board (pcb) component recognition and how can we address it? Cryptography 5(1), 9 (2021). https://doi.org/10.3390/cryptography5010009

    Article  Google Scholar 

  18. Wang, H., Li, M., Ma, F., Huang, S.L., Zhang, L.: Unsupervised anomaly detection via generative adversarial networks: poster abstract. In: Proceedings of the 18th International Conference on Information Processing in Sensor Networks, New York, NY, USA, pp. 313–314 (2019). https://doi.org/10.1145/3302506.3312605

  19. Kuo, C.W., Ashmore, J., Huggins, D., Kira, Z.: Data-efficient graph embedding learning for PCB component detection. In: WACV19 (2018). https://doi.org/10.1109/WACV.2019.00064

  20. Lim, D., Kim, Y., Park, T.: SMD classification for automated optical inspection machine using convolution neural network. In: 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 395–398 (2019). https://doi.org/10.1109/IRC.2019.00072

Download references

Author information

Authors and Affiliations

  1. School of Technology, State University of Campinas, Limeira, Brazil

    Marcos Antônio Andrade, Leandro Ronchini **menes & Rangel Arthur

  2. School of Electrical and Computer Engineering, State University of Campinas, Campinas, Brazil

    Pedro Carlos Fazolino Pepe

Authors
  1. Marcos Antônio Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Carlos Fazolino Pepe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leandro Ronchini **menes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rangel Arthur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcos Antônio Andrade .

Editor information

Editors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Unicamp, Campinas, São Paulo, Brazil

    Yuzo Iano

  2. Divisao de Engenharia Eletronica, Instituto Tecnologico de Aeronautica, Sao Jose dos Campos, São Paulo, Brazil

    Osamu Saotome

  3. Urb. Pampas de Santa Teresa, Dpto. E6-402, Santiago de Surco, Lima, Peru

    Guillermo Leopoldo Kemper Vásquez

  4. Pontifícia Universidade Católica de Campinas, Campinas, Brazil

    Claudia Cotrim Pezzuto

  5. Faculty of Electrical and Computer Engineering, Unicamp, Campinas, São Paulo, Brazil

    Rangel Arthur

  6. Universidade Estadual de Campinas, Hortolândia, Brazil

    Gabriel Gomes de Oliveira

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Andrade, M.A., Pepe, P.C.F., **menes, L.R., Arthur, R. (2022). A Survey on Automatic Inspection for Printed Circuit Board Analysis. In: Iano, Y., Saotome, O., Kemper Vásquez, G.L., Cotrim Pezzuto, C., Arthur, R., Gomes de Oliveira, G. (eds) Proceedings of the 7th Brazilian Technology Symposium (BTSym’21). BTSym 2021. Smart Innovation, Systems and Technologies, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-031-08545-1_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08545-1_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08544-4

  • Online ISBN: 978-3-031-08545-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation