In cooperation with the Iranian Nuclear Society

Document Type : Research Paper

Authors

1 Leading Materials Organization, Nuclear Science and Technology Research Institute, AEOI, P.O.BOX: 14395-836, Tehran-Iran

2 Department of Mathematics, Faculty of Basic Sciences, Bu-Ali Sina University, P.O.Box: 65178-38695, Hamedan - Iran

3 Department of Physics, Faculty of Basic Sciences, Imam Khomeini International University, P.O.BOX: 34148-96818, Qazvin - Iran

4 Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box: 14155-1339, Tehran - Iran

5 Iran Nuclear Regulatory Authority, AEOI, P.O.Box: 14155-1339, Tehran - Iran

Abstract

A large number of printed board circuits (PCBs) are analyzed by X-ray imaging in reverse engineering for the purpose of identifying their connections, their damage, and how they are linked together. Using this method, an X-ray is passed through single or multi-layer boards in order to determine how they are connected and how the parts are arranged. According to the findings, there is fogging in the radiographs due to X-ray scattering and the thinness of copper connections on the boards as well as the small sizes of electronic components. This research employs the Modified Total Variation method (MTV) with alternating gradient, which is an iterative method based on gradient changes in Total Variation, to increase contrast. Based on the results of the implementation of the MTV algorithm on radiographs of various ranges, the contrast has increased, and besides the copper connections, the internal components of the electronic components have also become clearer. Reconstructed images show a contrast improvement of approximately 20 to 40%, according to experts. Using this information, it is possible to repair or build boards in reverse engineering.

Highlights

  1. Azin A, Zhukov A, Narikovich A, Ponomarev S, Rikkonen S, Leitsin V. Nondestructive testing method for a new generation of electronics. MATEC Web of Conferences. 2018;143:04007. YSSIP-2017 https://doi.org/10.1051/matecconf/201814304007.

 

  1. Lu X, He Z, Su L, Fan M, Liu F, Liao G, Shi T. Detection of micro solder balls using active thermography technology and k-means algorithm. IEEE Trans Ind Inf. 2018;14(12):5620–5628.

 

  1. Liu S, Ume I.C. Vibration analysis based modeling and defect recognition for flip-chip solder-joint inspection. J. Electron. Packag. 2002;124:221–226.

 

  1. Liu S, Erdahl D, Ume C, Achari A. A novel method and device for solder joint quality inspection by using laser ultrasound. In Proceedings of the Electronic Components and Technology Conference. Las Vegas, NV, USA. 2000 21–24 May;408–415.

 

  1. Huang CY, Hong JH, Huang E. Developing a machine vision inspection system for electronics failure analysis. IEEE Trans Compon Packag Manuf Technol. 2019;9(9):1912–1925.

 

  1. Aryan P, Sampath S, Sohn H. An Overview of Non-Destructive Testing Methods for Integrated Circuit Packaging Inspection. Sensors. 2018;18:1981. doi:10.3390/s18071981.

 

  1. Bansal A, Ramakrishna G, Liu K. A New Approach for Early Detection of PCB Pad Cratering Failures. Cisco Systems Inc., San Jose, CA. 2011.

 

  1. Fu Z, Goyal D, Thomas J, Crawley A, Ramsey A. 3D X-ray computed tomography (CT) for electronic packages. Proc. of the 29th International Symposium for Testing and Failure Analysis. Santa Clara, CA (USA). 2003.

 

  1. Global Industry Analysts. Inc, Industrial X-Ray Inspection Systems-Market Analysis. Trends, and Forecasts. USA. 2019.

 

  1. ASTM E801-21, Standard Practice for Controlling Quality of Radiographic Examination of Electronic Devices. ASTM International. 2021.

 

  1. ASTM E1161-21. Standard Practice for Radiologic Examination of Semiconductors and Electronic Components. ASTM International. 2021.

 

  1. Zhang Q, Zhang M, Gamanayake Ch, Yuen Ch, Geng Z, Jayasekara H, Woo Ch.-w, Low J, Liu X, Guan Y.L. Deep learning based solder joint defect detection on industrial printed circuit board X-ray images, Complex & Intelligent Systems. 2022;8:1525–1537. https://doi.org/10.1007/ s40747-021-00600-w.

 

  1. Villaraga-Gómez H, Bell J.D. Modern 2D & 3D X-ray technologies for testing and failure analysis, ISTFA Proceedings, 45th International Symposium for Testing and Failure Analysis. November. 2019. DOI: 10.31399/asm.cp.istfa2019p0014.

 

  1. Yahaghi E, Mirzapour M, Movafeghi A. Enhancing flaw detection in aluminum castings by two different mixed noise removal methods. Phys. Scr. 2020;95: 075302. 9. https://doi.org/10.1088/1402-4896/ ab8d00.

 

  1. Yahaghi E, Hosseini-Ashrafi M.E, Comparison of the performance of three domain transform filters for radiographic contrast enhancement of welded objects. Insight. 2020 June;62(6).

 

  1. Rudin Leonid I, Stanley Osher, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena. 1992;60(1-4):259-268.

 

  1. Chambolle Antonin, Stacey E. Levine, Bradley J. Lucier. An upwind finite-difference method for total variation–based image smoothing. SIAM Journal on Imaging Sciences. 2011;4(1):277-299.

 

  1. Abergel R, Moisan L. The Shannon total variation. Journal of Mathematical Imaging and Vision. 2017;59(2):341-370.

 

  1. ISO-17636-2. Non-destructive Testing of Welds—Radiographic Testing—Part 2: X- and Gamma-Ray Techniques with Digital Detectors. International Organization for Standardization. Geneva. 2013.

 

  1. EN-12681-2. Founding. Radiographic Testing. Techniques with Digital Detectors. European Standards Organization. 2017.

 

  1. Condat L. Discrete total variation: New definition and minimization. SIAM Journal on Imaging Sciences. 2017;10(3):1258-1290.

 

  1. Shiqian Ma. Alternating proximal gradient method for convex minimization. Journal of Scientific Computing. 2016;68(2):546-572.

 

  1. Deng W, Yin W. On the global and linear convergence of the generalized alternating direction method of multipliers. Journal of Scientific Computing. 2016;66(3):889-916.

Keywords

  1. Azin A, Zhukov A, Narikovich A, Ponomarev S, Rikkonen S, Leitsin V. Nondestructive testing method for a new generation of electronics. MATEC Web of Conferences. 2018;143:04007. YSSIP-2017 https://doi.org/10.1051/matecconf/201814304007.

 

  1. Lu X, He Z, Su L, Fan M, Liu F, Liao G, Shi T. Detection of micro solder balls using active thermography technology and k-means algorithm. IEEE Trans Ind Inf. 2018;14(12):5620–5628.

 

  1. Liu S, Ume I.C. Vibration analysis based modeling and defect recognition for flip-chip solder-joint inspection. J. Electron. Packag. 2002;124:221–226.

 

  1. Liu S, Erdahl D, Ume C, Achari A. A novel method and device for solder joint quality inspection by using laser ultrasound. In Proceedings of the Electronic Components and Technology Conference. Las Vegas, NV, USA. 2000 21–24 May;408–415.

 

  1. Huang CY, Hong JH, Huang E. Developing a machine vision inspection system for electronics failure analysis. IEEE Trans Compon Packag Manuf Technol. 2019;9(9):1912–1925.

 

  1. Aryan P, Sampath S, Sohn H. An Overview of Non-Destructive Testing Methods for Integrated Circuit Packaging Inspection. Sensors. 2018;18:1981. doi:10.3390/s18071981.

 

  1. Bansal A, Ramakrishna G, Liu K. A New Approach for Early Detection of PCB Pad Cratering Failures. Cisco Systems Inc., San Jose, CA. 2011.

 

  1. Fu Z, Goyal D, Thomas J, Crawley A, Ramsey A. 3D X-ray computed tomography (CT) for electronic packages. Proc. of the 29th International Symposium for Testing and Failure Analysis. Santa Clara, CA (USA). 2003.

 

  1. Global Industry Analysts. Inc, Industrial X-Ray Inspection Systems-Market Analysis. Trends, and Forecasts. USA. 2019.

 

  1. ASTM E801-21, Standard Practice for Controlling Quality of Radiographic Examination of Electronic Devices. ASTM International. 2021.

 

  1. ASTM E1161-21. Standard Practice for Radiologic Examination of Semiconductors and Electronic Components. ASTM International. 2021.

 

  1. Zhang Q, Zhang M, Gamanayake Ch, Yuen Ch, Geng Z, Jayasekara H, Woo Ch.-w, Low J, Liu X, Guan Y.L. Deep learning based solder joint defect detection on industrial printed circuit board X-ray images, Complex & Intelligent Systems. 2022;8:1525–1537. https://doi.org/10.1007/ s40747-021-00600-w.

 

  1. Villaraga-Gómez H, Bell J.D. Modern 2D & 3D X-ray technologies for testing and failure analysis, ISTFA Proceedings, 45th International Symposium for Testing and Failure Analysis. November. 2019. DOI: 10.31399/asm.cp.istfa2019p0014.

 

  1. Yahaghi E, Mirzapour M, Movafeghi A. Enhancing flaw detection in aluminum castings by two different mixed noise removal methods. Phys. Scr. 2020;95: 075302. 9. https://doi.org/10.1088/1402-4896/ ab8d00.

 

  1. Yahaghi E, Hosseini-Ashrafi M.E, Comparison of the performance of three domain transform filters for radiographic contrast enhancement of welded objects. Insight. 2020 June;62(6).

 

  1. Rudin Leonid I, Stanley Osher, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena. 1992;60(1-4):259-268.

 

  1. Chambolle Antonin, Stacey E. Levine, Bradley J. Lucier. An upwind finite-difference method for total variation–based image smoothing. SIAM Journal on Imaging Sciences. 2011;4(1):277-299.

 

  1. Abergel R, Moisan L. The Shannon total variation. Journal of Mathematical Imaging and Vision. 2017;59(2):341-370.

 

  1. ISO-17636-2. Non-destructive Testing of Welds—Radiographic Testing—Part 2: X- and Gamma-Ray Techniques with Digital Detectors. International Organization for Standardization. Geneva. 2013.

 

  1. EN-12681-2. Founding. Radiographic Testing. Techniques with Digital Detectors. European Standards Organization. 2017.

 

  1. Condat L. Discrete total variation: New definition and minimization. SIAM Journal on Imaging Sciences. 2017;10(3):1258-1290.

 

  1. Shiqian Ma. Alternating proximal gradient method for convex minimization. Journal of Scientific Computing. 2016;68(2):546-572.

 

  1. Deng W, Yin W. On the global and linear convergence of the generalized alternating direction method of multipliers. Journal of Scientific Computing. 2016;66(3):889-916.