Skip to main content
SpringerLink
Log in

Advancing surface mount technology quality: a computer-assisted approach for enhanced X-ray inspection of solder joints

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

With the constant advancement of electronic science and technology, accurate failure analysis has become crucial to meeting stringent quality standards in surface mount (SMT) products. Reflow soldering is commonly used in the SMT industry due to its efficiency and low failure rate. However, visual inspection is limited, especially for ball grid array (BGA) joints, requiring X-ray techniques for complete, non-destructive analysis. Although X-ray image analysis algorithms have been employed to improve defect detection, some still need to meet quality requirements, resulting in additional manual inspections. The high dimensionality and variations in X-ray images present additional challenges for detection algorithms. This study proposes a new computer-assisted inspection approach to accurately detect flaws in solder joints of SMD components using an X-ray scanning system. This approach aims to improve image interpretation and reduce workload manual. X-ray technology applied to BGA seeks to improve the accuracy of image analysis, enabling the detection of various faults, such as BGA ball connections, interconnections on printed circuit boards, and filling faults.

Graphical Abstract

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Tsai T-N (2012) Thermal parameters optimization of a reflow soldering profile in printed circuit board assembly: a comparative study. Appl Soft Comput 12(8):2601–2613

    Article  Google Scholar 

  2. Huang X, Zhu S, Huang X, Su B, Ou C, Zhou W (2015) Detection of plated through hole defects in printed circuit board with Xray. In: 2015 16th international conference on electronic packaging technology (ICEPT). IEEE, pp 1296–1301

  3. Xiao P, Xiao M, Cai N, Qiu B, Zhou S, Wang H (2023) Adaptive hybrid framework for multiscale void inspection of chip resistor solder joints. IEEE Trans Instrum Meas 72:1–12

    Google Scholar 

  4. Ma JQ, Kong FH, Ma PJ, Su XH (2005) Detection of defects at BGA solder joints by using X-ray imaging. In: 2005 international conference on machine learning and cybernetics, vol 8. IEEE, pp 5139–5143

  5. Zhang Qianru et al (2022) Deep learning based solder joint defect detection on industrial printed circuit board X-ray images. Complex Intell Syst 8(2):1525–1537

    Article  Google Scholar 

  6. Liu A, Zou C, Lin T, Li J, Tan CK, Feng ZJ et al (2016) X-ray inspection methods for controlling PCBA potting process—2DX and partial angle computer tomography. In: 2016 pan pacific microelectronics symposium (Pan Pacific). IEEE, pp 1–5

  7. Asadizanjani N, Tehranipoor M, Forte D (2017) PCB reverse engineering using nondestructive X-ray tomography and advanced image processing. IEEE Trans Compon Packag Manuf Technol 7(2):292–299

    Google Scholar 

  8. Wang Y, Wang M, Zhang Z (2014) Microfocus X-ray printed circuit board inspection system. Optik 125(17):4929–4931

    Article  Google Scholar 

  9. Li X, Liu G, Sun S, Yi W, Li B (2023) Digital twin model-based smart assembly strategy design and precision evaluation for PCB kit-box build. J Manuf Syst 71:206–223

  10. Fang T, An J, Chen Q, He Y, Wang H, Zhang X (2023) Progress and comparison in nondestructive detection, imaging and recognition technology for defects of wafers, chips and solder joints. Nondestructive Testing and Evaluation (NDT&E):1–56

  11. Wang F, Yue Z, Liu J, Qi H, Sun W, Chen M et al (2022) Quantitative imaging of printed circuit board (PCB) delamination defects using laser-induced ultrasound scanning imaging. J Appl Phys 131(5):053101

  12. Lin CY, Li CL, Kuo YC, Cheng YC, Jian CY, Huang HT, Hsu MM (2023) A deep learning-based microsection measurement framework for print circuit boards. In: 2023 IEEE international conference on industry 4.0, artificial intelligence, and communications technology (IAICT). IEEE, pp 291–294

  13. Casali DR, Heath G, Nad S (2019) Using fundamental concepts to determine how to monitor dye penetrant use. J Fail Anal Prev 19:45–52

    Article  Google Scholar 

  14. Jeon M, Yoo S, Kim SW (2022) A contactless PCBA defect detection method: convolutional neural networks with thermographic images. IEEE Trans Compon Packag Manuf Technol 12(3):489–501

    Article  Google Scholar 

  15. Houdek C, Design C (2016) Inspection and testing methods for PCBs: an overview. Engineer/OwnerCaltronics Design & Assembly, p 401. Available in: https://static1.squarespace.com/static/62e3c6284ddd8f7896c21906/t/643ea484dd7ee32ad15a78b7/1681826950765/CD%26A+White+Paper+%23401.pdf

  16. Mehta D, True J, Dizon-Paradis OP, Jessurun N, Woodard DL, Asadizanjani N, Tehranipoor M (2022) FICS PCB X-ray: a dataset for automated printed circuit board inter-layers inspection. Cryptology ePrint Archive. Available in: https://eprint.iacr.org/2022/924

  17. Dai W, Mujeeb A, Erdt M, Sourin A (2020) Soldering defect detection in automatic optical inspection. Adv Eng Inform 43:101004

    Article  Google Scholar 

  18. Abd Al Rahman M, Mousavi A (2020) A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry. IEEE Access 8:183192–183271

    Article  Google Scholar 

  19. Wanchun T, Qin W, Liang Z (2018) Failure analysis technology of lead-free BGA solder joints and relevant cases. In: 2018 19th international conference on electronic packaging technology (ICEPT). IEEE, pp 284–287

  20. Fidan I, Roush E, Tumkor S, Kraft RP (2006) Internet-based electronics manufacturing troubleshooting tool for surface mount PCB assembly. Int J Adv Manuf Technol 27:561–567

    Article  Google Scholar 

  21. Said AF, Bennett BL, Karam LJ, Siah A, Goodman K, Pettinato JS (2012) Automated void detection in solder balls in the presence of vias and other artifacts. IEEE Trans Compon Packag Manuf Technol 2(11):1890–1901

    Article  Google Scholar 

  22. Peng SH, Nam HD (2012) Void defect detection in ball grid array X-ray images using a new blob filter. J Zhejiang Univ Sci C 13:840–849

    Article  Google Scholar 

  23. Mouri M, Kato Y, Yasukawa H, Takumi I (2014) A study of using nonnegative matrix factorization to detect solder-voids from radiographic images of solder. In: 2014 IEEE 23rd international symposium on industrial electronics (ISIE). IEEE, pp 1074–1079

  24. Nuanprasert S, Baba S, Suzuki T (2015) An efficient method of occluded solder ball segmentation for automated BGA void defect inspection using X-ray images. In: IECON 2015–41st annual conference of the IEEE industrial electronics society. IEEE, pp 003308–003313

  25. Nuanprasert S, Baba S, Suzuki T (2015) A simple automated void defect detection for poor contrast x-ray images of BGA. In: The 3rd international conference on industrial application engineering, Kitakyushu. Available in: https://pdfs.semanticscholar.org/870c/1b643bf7a02ff9d04af0f06d592bbe55f771.pdf

  26. Chiou YC, Lin CS, Chiou BC (2008) The feature extraction and analysis of flaw detection and classification in BGA gold-plating areas. Expert Syst Appl 35(4):1771–1779

    Article  Google Scholar 

  27. LaCasse PM, Otieno W, Maturana FP (2020) Predicting contact-without-connection defects on printed circuit boards employing ball grid array package types: a data analytics case study in the smart manufacturing environment. SN Appl Sci 2:1–13

    Article  Google Scholar 

  28. Xie Y, Ye Y, Zhang J, Liu L, Liu L (2014) A physics-based defects model and inspection algorithm for automatic visual inspection. Opt Lasers Eng 52:218–223

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Postgraduate Program in Industrial Engineering, Federal University of Amazonas, Av. General Rod. O.J. Ramos, 6200, Manaus, 69080-900, Brazil

    Nathália Mattos Terra, Sandro Breval Santiago & Raimundo Kennedy Vieira

  2. Federal University of Amazonas, Manaus, 69080-900, Brazil

    Adalena Kennedy Vieira

Authors
  1. Nathália Mattos Terra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandro Breval Santiago
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adalena Kennedy Vieira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raimundo Kennedy Vieira
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Nathália Mattos Terra, Sandro Breval Santiago, Adalena Kennedy Vieira, and Raimundo Kennedy Vieira. The first draft of the manuscript was written by Nathália Mattos Terra and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Raimundo Kennedy Vieira.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Terra, N.M., Santiago, S.B., Vieira, A.K. et al. Advancing surface mount technology quality: a computer-assisted approach for enhanced X-ray inspection of solder joints. Int J Adv Manuf Technol 131, 5897–5904 (2024). https://doi.org/10.1007/s00170-024-13343-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-024-13343-y

Keywords

Search

Navigation