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A Comprehensive Taxonomy of Visual Printed Circuit Board Defects

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Abstract

The globalization of printed circuit board (PCB) production has expanded the avenues to introduce vulnerabilities into the electronics supply chain. Malicious parties may modify or alter PCBs to purposely compromise the integrity of a design. In contrast, non-malicious incidents may also change an ideal design; they unintentionally lead to defects. For the reverse engineering process, which requires precise information to replicate a design, these deviations from the intended design present an overwhelming challenge. Although they are primarily unintentional, defects may cause a device to stray from its intended functionality, and present danger to both manufacturers and consumers. Therefore, irrespective of their cause and effect, the PCB inspection industry requires intricate knowledge of their occurrences; defects must be correctly enumerated before satisfactory solutions can be proposed. This article presents an extensive taxonomy of physical defects in PCBs that can be primarily identified using automated optical inspection (AOI), or traditional visual inspection methods where applicable. Our goal is to ultimately improve the state of hardware security by providing a guide to physical defects. To the best of our knowledge, this is the first comprehensive taxonomy of visual PCB defects.

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References

  1. (ARC) IARC. Printed circuit board market; recyclable pcb market share, size and industry growth analysis 2021 - 2026. https://www.industryarc.com/Report/110/Printed-Circuit-Board-Market-Analysis-and-Forecast.html. Accessed 3 Jan 2022

  2. Publishing B (2018) Printed circuit Boards: Technologies and global markets. https://www.bccresearch.com/market-research/semiconductor-manufacturing/printed-circuit-boards-tech-markets-report.html

  3. Mehta D, Lu H, Paradis OP, M S MA, Rahman MT, Iskander Y, Chawla P, Woodard DL, Tehranipoor M, Asadizanjani N (2020) The big hack explained: Detection and prevention of pcb supply chain implants. J Emerg Technol Comput Syst 16(4). https://doi.org/10.1145/3401980, https://doi.org/10.1145/3401980

  4. Azhagan M S M, Mehta D, Lu H, Agrawal S, Tehranipoor M, Woodard DL, Asadizanjani N, Chawla P (2019) A Review on Automatic Bill of Material Generation and Visual Inspection on PCBs. In: ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis, International Symposium for Testing and Failure Analysis, pp 256–265. https://doi.org/10.31399/asm.cp.istfa2019p0256, https://dl.asminternational.org/istfa/proceedings-pdf/ISTFA2019/82747/256/417158/istfa2019p0256.pdf

  5. Intelligence M. Printed circuit board inspection equipment market: 2021 - 26: Industry share, size, growth - mordor intelligence. https://www.mordorintelligence.com/industry-reports/printed-circuit-board-inspection-equipment-market. Accessed 3 Jan 2022

  6. ViTrox (2022) Automated machine vision inspection system provider. https://www.vitrox.com/

  7. Viscom (2022) Inspection systems for the electronics industry. https://www.viscom.com/en/

  8. Wu WY, Wang MJJ, Liu CM (1996) Automated inspection of printed circuit boards through machine vision. Comput Ind 28(2):103–111. https://doi.org/10.1016/0166-3615(95)00063-1,https://www.sciencedirect.com/science/article/pii/0166361595000631

  9. Dave N, Tambade V, Pandhare B, Saurav S (2016) Pcb defect detection using image processing and embedded system. Int Res J Eng Technol 3(5), 1897–1901

    Google Scholar 

  10. Gaidhane VH, Hote YV, Singh V (2018) An efficient similarity measure approach for pcb surface defect detection. Pattern Anal Applic 21(1):277–289

    Article  MathSciNet  Google Scholar 

  11. Malge P, Nadaf R (2014) Pcb defect detection, classification and localization using mathematical morphology and image processing tools. Int J Comput Appl 87(9)

  12. Putera SI, Ibrahim Z (2010) Printed circuit board defect detection using mathematical morphology and matlab image processing tools. In: 2010 2nd international conference on education technology and computer, IEEE, vol 5, pp V5–359

  13. Tang S, He F, Huang X, Yang J (2019) Online pcb defect detector on a new pcb defect dataset. 1902.06197

  14. Tsai DM, Yang RH (2005) An eigenvalue-based similarity measure and its application in defect detection. Image Vis Comput 23(12):1094–1101. https://doi.org/10.1016/j.imavis.2005.07.014, https://www.sciencedirect.com/science/article/pii/S0262885605001228

  15. Botero UJ, Wilson R, Lu H, Rahman MT, Mallaiyan MA, Ganji F, Asadizanjani N, Tehranipoor MM, Woodard DL, Forte D (2020) Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives. arXiv:200204210 [cs, eess] http://arxiv.org/abs/2002.04210, arXiv: 2002.04210

  16. Jawitz MW (1997) Printed Circuit Board Materials Handbook. McGraw-Hill

    Google Scholar 

  17. Huang C, Liao L (2007) An intelligent agent-based collaborative workflow for inter-enterprise pcb product design. In: 2007 IEEE International Conference on Industrial Engineering and Engineering Management, pp 189–193. https://doi.org/10.1109/IEEM.2007.4419177

  18. Long M, editor MLia, Follow (2021) What is a pcb. https://www.electromaker.io/blog/article/what-is-a-pcb

  19. Verbelen Y, Belle D, Tiete J (2013) Experimental analysis of small scale pcb manufacturing techniques for fablabs. International Journal of Engineering Innovation and Research 2:136–143

    Google Scholar 

  20. Co C (2022) Mouse pcb inspection. https://www.cognex.com/industries/electronics/pcb-assembly/mouse-pcb-inspection

  21. PCBCart P (2022) Pcb manufacturing process - a step-by-step guide. https://www.pcbcart.com/article/content/PCB-manufacturing-process.html

  22. Millenium C (2022) An ultimate guide to the pcb manufacturing process. https://www.mclpcb.com/blog/pcb-manufacturing-process/#steptwo

  23. Hults C, Schwedner F, Grossman S (1975) In-circuit test systems-an evolution. IEEE Trans Manuf Technol 4(2):42–48. https://doi.org/10.1109/TMFT.1975.1135859

    Article  Google Scholar 

  24. Howden WE (1979) Functional testing and design abstractions. J Syst Softw 1:307–313. https://doi.org/10.1016/0164-1212(79)90032-3, https://www.sciencedirect.com/science/article/pii/0164121279900323

  25. IEEE (2013) Ieee standard for test access port and boundary-scan architecture. IEEE Std 11491-2013 (Revision of IEEE Std 11491-2001) pp 1–444. https://doi.org/10.1109/IEEESTD.2013.6515989

  26. Bhunia S, Tehranipoor M (2018) Hardware Security: A Hands-on Learning Approach, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

    Google Scholar 

  27. Mahmoud Taha E, Emary E, Moustafa K (2014) Automatic optical inspection for pcb manufacturing: a survey. International Journal of Scientific and Engineering Research Volume 5:1095–1102

  28. Asadizanjani N, Rahman MT, Tehranipoor M (2021) Physical Inspection of Printed Circuit Boards, Springer International Publishing, Cham, pp 67–99. https://doi.org/10.1007/978-3-030-62609-9_4

  29. Alavi S, Yazawa K, Alers G, Vermeersch B, Christofferson J, Shakouri A (2011) Thermal imaging for reliability characterization of copper vias. In: 2011 27th Annual IEEE Semiconductor Thermal Measurement and Management Symposium, pp 17–20. https://doi.org/10.1109/STHERM.2011.5767172

  30. Michalski A, Sawallich S, True J, Asadizanjani N, Nagel M (2021) High-resolution terahertz near-field reflection measurements for complementary non-destructive inspection of integrated circuits. In: 2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz), pp 1–2. https://doi.org/10.1109/IRMMW-THz50926.2021.9567363

  31. Brizuela F, Wang Y, Brewer CA, Pedaci F, Chao W, Anderson EH, Liu Y, Goldberg KA, Naulleau P, Wachulak P, Marconi MC, Attwood DT, Rocca JJ, Menoni CS (2009) 13.2 nm table-top inspection microscope for extreme ultraviolet lithography mask defect characterization. In: 2009 Conference on Lasers and Electro-Optics and 2009 Conference on Quantum electronics and Laser Science Conference, pp 1–2. https://doi.org/10.1364/CLEO.2009.JFA5

  32. Yin A (2012) Analysis of optical inspection from aoi and avi machines. In: IPC APEX EXPO Proceedings

  33. Huang W, Wei P (2019) A pcb dataset for defects detection and classification. 1901.08204

  34. Jessurun N, Dizon-Paradis OP, Harrison J, Ghosh S, Tehranipoor MM, Woodard DL, Asadizanjani N (2022) Fpic: A novel semantic dataset for optical pcb assurance. 2202.08414

  35. Kampel M, Pramerdorfer C (2020) Pcb dslr dataset. https://doi.org/10.5281/zenodo.3886553, https://doi.org/10.5281/zenodo.3886553

  36. Mahalingam G, Gay KM, Ricanek K (2019) Pcb-metal: A pcb image dataset for advanced computer vision machine learning component analysis. In: 2019 16th International Conference on Machine Vision Applications (MVA), pp 1–5. https://doi.org/10.23919/MVA.2019.8757928

  37. Cheong LK, Suandi SA, Rahman S (2019) Defects and components recognition in printed circuit boards using convolutional neural network. In: Zawawi MAM, Teoh SS, Abdullah NB, Mohd Sazali MIS (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications, Springer Singapore, Singapore, pp 75–81

    Google Scholar 

  38. Khalilian S, Hallaj Y, Balouchestani A, Karshenas H, Mohammadi A (2020) Pcb defect detection using denoising convolutional autoencoders. 2020 International Conference on Machine Vision and Image Processing (MVIP) pp 1–5

  39. Shen J, Liu N, Sun H (2020) Defect detection of printed circuit board based on lightweight deep convolution network. IET Image Process 14(15):3932–3940. https://doi.org/10.1049/iet-ipr.2020.0841, https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/iet-ipr.2020.0841, https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2020.0841

  40. Li Cj, Qu Z, Wang Sy, Bao Kh, Wang Sy (2021) A method of defect detection for focal hard samples pcb based on extended fpn model. IEEE Transactions on Components, Packaging and Manufacturing Technology, pp 1–1. https://doi.org/10.1109/TCPMT.2021.3136823

  41. IPCTraining (2020) 188c - printed circuit board defects. https://www.youtube.com/watch?v=EGxKNp2ZQmA

  42. Araujo K, Epec L. Wave soldering defects. https://www.epectec.com/pcb/wave-soldering-defects/. Accessed 3 Jan 2022

  43. Foresite. What is a flux residue? https://www.foresiteinc.com/resources/what-is-a-flux-residue. Accessed 3 Jan 2022

  44. TechnoLab. https://www.technolab.de/en/solder-dictionary.php. Accessed 3 Jan 2022

  45. Helen (2021) 13 common pcb soldering problems to avoid. https://www.seeedstudio.com/blog/2021/06/18/13-common-pcb-soldering-problems-to-avoid/

  46. Dai W, Mujeeb A, Erdt M, Sourin A (2020) Soldering defect detection in automatic optical inspection. Adv Eng Inf 43:101004. https://doi.org/10.1016/j.aei.2019.101004, https://www.sciencedirect.com/science/article/pii/S1474034619305774

  47. Shashikanth R (2022) Annular ring explained by a pcb manufacturer. https://www.protoexpress.com/blog/dont-let-annular-rings-drive-you-crazy/

  48. Demarest K. https://people.eecs.ku.edu/~a454g185/eecs212_lab/Labs/Filter_Design_Lab/PCB_Etching/etching.pdf. Accessed 3 Jan 2022

  49. Mujeeb A, Dai W, Erdt M, Sourin A (2019) One class based feature learning approach for defect detection using deep autoencoders. Adv Eng Inform 42:100933. https://doi.org/10.1016/j.aei.2019.100933, https://www.sciencedirect.com/science/article/pii/S1474034619301259

  50. Staff D. 3 most common pcb assembly defects. https://blog.thedigisource.com/3-common-pcba-defects. Accessed 3 Jan 2022

  51. Ltd S (2019) Pcb inspection methods & common pcb defects. https://www.youtube.com/watch?v=5WuScHKaz8o%5C &t=627s

  52. Ding R, Dai L, Li G, Liu H (2019) Tdd-net: a tiny defect detection network for printed circuit boards. CAAI Trans Intell Technol 4:110–116

    Article  Google Scholar 

  53. Department DP (2020) Component orientation on pcbs: Best practices to optimize assembly. https://www.vse.com/blog/2020/01/21/

  54. Rau H, Wu CH (2005) Automatic optical inspection for detecting defects on printed circuit board inner layers. Int J Adv Manuf Technol 25:940–946. https://doi.org/10.1007/s00170-004-2299-9

    Article  Google Scholar 

  55. Gaidhane VH, Hote YV, Singh V (2017) An efficient similarity measure approach for pcb surface defect detection. Pattern Anal Applic 21:277–289

    Article  MathSciNet  Google Scholar 

  56. Chen TQ, Zhang J, Zhou Y, Murphey YL (2001) A smart machine vision system for pcb inspection. In: Proceedings of the 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: Engineering of Intelligent Systems, Springer-Verlag, Berlin, Heidelberg, IEA/AIE ’01, p 513–518

  57. Nordson (2022) Adhesive dispensing equipment manufacturers, coating equipment, fluid dispensing systems. https://www.nordson.com/en

  58. Porto (2022) One-stop pcb assembly services. https://www.7pcb.com/

  59. ipc (2020) What does the ipc 6011 standard mean to pcb manufacturing? https://blog.matric.com/what-does-the-ipc-6011-standard-mean-to-pcb

  60. IPC-RB-276 S (1996) ipc. https://www.ipc.org/TOC/IPC-6011.pdf

  61. Kim J, Ko J, Choi H, Kim H (2021) Printed circuit board defect detection using deep learning via a skip-connected convolutional autoencoder. Sensors 21(15). https://doi.org/10.3390/s21154968, https://www.mdpi.com/1424-8220/21/15/4968

  62. Li YT, Kuo P, Guo JI (2020) Automatic industry pcb board dip process defect detection with deep ensemble method. In: 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), pp 453–459. https://doi.org/10.1109/ISIE45063.2020.9152533

  63. Raihan F, Ce W (2017) Pcb defect detection using opencv with image subtraction method. In: 2017 International Conference on Information Management and Technology (ICIMTech), pp 204–209. https://doi.org/10.1109/ICIMTech.2017.8273538

  64. Dai W, Mujeeb A, Erdt M, Sourin A (2018) Towards automatic optical inspection of soldering defects. In: 2018 International Conference on Cyberworlds (CW), pp 375–382. https://doi.org/10.1109/CW.2018.00074

  65. Liu Z, Qu B (2021) Machine vision based online detection of pcb defect. Microprocess Microsyst 82:103807. https://doi.org/10.1016/j.micpro.2020.103807, https://www.sciencedirect.com/science/article/pii/S0141933120309522

  66. Nayak JPR, Parameshachari BD, Soyjaudah KMS, Rajashekarappa, Banu R, Nuthan AC (2017) Identification of pcb faults using image processing. In: 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp 1–4. https://doi.org/10.1109/ICEECCOT.2017.8284602

  67. Azhagan M, Mehta D, Lu H, Agrawal S, Tehranipoor M, Woodard D, Asadizanjani N, Chawla P (2019) A review on automatic bill of material generation and visual inspection on pcbs. In: ISTFA 2019: Proceedings of the 45th International Symposium for Testing and Failure Analysis, pp 256–265. https://doi.org/10.31399/asm.cp.istfa2019p0256

  68. Ma J (2017) Defect detection and recognition of bare pcb based on computer vision. In: 2017 36th Chinese Control Conference (CCC), pp 11023–11028. https://doi.org/10.23919/ChiCC.2017.8029117

  69. Zhang C, Shi W, Li X, Zhang H, Liu H (2018) Improved bare pcb defect detection approach based on deep feature learning. J Eng

  70. Xia SY, Wang F, Xie F, Huang L, Wang Q, Ling X (2021) An efficient and robust target detection algorithm for identifying minor defects of printed circuit board based on phfe and fl-rfcn. In: Frontiers in Physics

  71. Lee WH, Ozger M, Challita U, Sung KW (2021) Noise learning-based denoising autoencoder. IEEE Commun Lett 25(9):2983–2987. https://doi.org/10.1109/lcomm.2021.3091800, http://dx.doi.org/10.1109/LCOMM.2021.3091800

  72. Fridman Y, Rusanovsky M, Oren G (2021) Changechip: A reference-based unsupervised change detection for pcb defect detection. In: 2021 IEEE Physical Assurance and Inspection of Electronics (PAINE), pp 1–8. https://doi.org/10.1109/PAINE54418.2021.9707699

  73. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. 1406.2661

  74. True J, Xi C, Jessurun N, Ahi K, Asadizanjani N (2021) Review of thz-based semiconductor assurance. Opt Eng 60(6):060901

    Article  Google Scholar 

  75. Houdek C, Design C (2016) Inspection and testing methods for pcbs: An overview. Engineer/OwnerCaltronics Design & Assembly

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Authors and Affiliations

  1. University of Florida, Gainesville, FL, 32611, USA

    David Selasi Koblah, Olivia P. Dizon-Paradis, Justin Schubeck, Ulbert J. Botero, Damon L. Woodard & Domenic Forte

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  1. David Selasi Koblah
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  2. Olivia P. Dizon-Paradis
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Correspondence to David Selasi Koblah.

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This work was supported by the Air Force Research Laboratory (AFRL) and Edaptive Computing, Inc. It is approved for public release under case number: AFRL-2022-4552.

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Authors A, B and C contributed text and literature reviews to the paper. Authors D, E and F provided supervision throughout the writing process. All authors reviewed the paper.

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Koblah, D.S., Dizon-Paradis, O.P., Schubeck, J. et al. A Comprehensive Taxonomy of Visual Printed Circuit Board Defects. J Hardw Syst Secur 7, 25–43 (2023). https://doi.org/10.1007/s41635-023-00132-4

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