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Sensors  2013 

Defect Inspection of Flip Chip Solder Bumps Using an Ultrasonic Transducer

DOI: 10.3390/s131216281

Keywords: flip chip, ultrasonic inspection, support vector machine, defect inspection

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Abstract:

Surface mount technology has spurred a rapid decrease in the size of electronic packages, where solder bump inspection of surface mount packages is crucial in the electronics manufacturing industry. In this study we demonstrate the feasibility of using a 230 MHz ultrasonic transducer for nondestructive flip chip testing. The reflected time domain signal was captured when the transducer scanning the flip chip, and the image of the flip chip was generated by scanning acoustic microscopy. Normalized cross-correlation was used to locate the center of solder bumps for segmenting the flip chip image. Then five features were extracted from the signals and images. The support vector machine was adopted to process the five features for classification and recognition. The results show the feasibility of this approach with high recognition rate, proving that defect inspection of flip chip solder bumps using the ultrasonic transducer has high potential in microelectronics packaging.

References

[1]  Brand, S.; Czurratis, P.; Hoffrogge, P.; Petzold, M. Automated inspection and classification of flip-chip-contacts using scanning acoustic microscopy. Microelectron. Reliab. 2010, 50, 1469–1473.
[2]  Tan, C.W.; Chan, Y.C.; Leung, B.; Liu, H.D. Effects of soft beam energy on the microstructure of Pb37Sn, Au20Sn, and Sn3.5Ag0.5Cu solder joints in lensed-sm-fiber to laser-diode-affixing application. Opt. Laser Eng. 2008, 46, 75–82.
[3]  Guo, F. Composite lead-free electronic solders. J. Mater. Sci. Mater. Electron. 2007, 18, 129–145.
[4]  Shih, T.I.; Lin, Y.C.; Duh, J.G.; Hsu, T. Electrical characteristics for Sn-Ag-Cu solder bump with Ti/Ni/Cu under-bump metallization after temperature cycling tests. J. Electron. Mater. 2006, 35, 1773–1780.
[5]  Steglich, D.; Siegmund, T.; Brocks, W. Micromechanical modeling of damage due to particle cracking in reinforced metals. Comput. Mater. Sci. 1999, 16, 404–413.
[6]  Lera, M.; Montisci, A. Neural Networks Based AOI Systems for Electronic Devices Diagnosis. Proceedings of 19th Congress of the International Commission for Optics: Optics for the Quality of Life, Firenze, Italy, 25–30 August 2002; pp. 859–861.
[7]  Giaquinto, A.; Fornarelli, G.; Brunetti, G.; Acciani, G. A neurofuzzy method for the evaluation of soldering global quality index. IEEE Tran. Ind. Inform. 2009, 5, 56–66.
[8]  Acciani, G.; Brunetti, G.; Fornarelli, G. Application of neural networks in optical inspection and classification of solder joints in surface mount technology. IEEE Tran. Ind. Inform. 2006, 2, 200–209.
[9]  Liu, S.; Ume, I.C.; Achari, A. Defects Pattern Recognition for Flip-Chip Solder Joint Quality Inspection with Laser Ultrasound and Interferometer. Proceedings of 52nd Electronic Components and Technology Conference, San Diego, CA, USA, 31 May 2002; pp. 1491–1496.
[10]  Lu, X.N.; Liao, G.L.; Zha, Z.Y.; Xia, Q.; Shi, T.L. A novel approach for flip chip solder joint inspection based on pulsed phase thermography. NDT E Int. 2011, 44, 484–489.
[11]  Chai, T.C.; Wong, B.S.; Bai, W.M.; Trigg, A.; Lam, Y.K. A Novel Defect Detection Technique Using Active Transient Thermography for High Density Package and Interconnections. Proceedings of 53rd Electronic Components & Technology Conference, New Orleans, LA, USA, 27–30 May 2003; pp. 920–925.
[12]  Chiu, S.H.; Chen, C. Investigation of void nucleation and propagation during electromigration of flip-chip solder joints using x-ray microscopy. Appl. Phys. Lett. 2006, 89, doi:10.1063/1.2425040.
[13]  Voci, F.; Eiho, S.; Sugimoto, N. Fuzzy Inference Filter and Morphological Operators for Short Circuits Detection in Printed Circuit Board. Proceedings of IEEE International Symposium on Industrial Electronics, L'Aquila, Italy, 8–11 July 2002; pp. 672–677.
[14]  Diego, C.; Hernandez, A.; Jimenez, A.; Alvarez, F.J.; Sanz, R.; Aparicio, J. Ultrasonic array for obstacle detection based on CDMA with kasami codes. Sensors 2011, 11, 11464–11475.
[15]  Lee, K.Y.; Lee, P.; Tan, A.M.; Lee, C. SAM Interpretation of Interfacial Anomaly in Flip-Chip BGA Package with 65 nm Cu/low-κ Integrated Circuits Device. Proceedings of 31st International Conference on Electronics Manufacturing and Technology, Petaling Jaya, 8–10 November 2007; pp. 269–274.
[16]  Semmens, J.E.; Kessler, L.W. Characterization of Flip Chip Interconnect Failure Modes Using High Frequency Acoustic Micro Imaging with Correlative Analysis. Proceedings of Reliability IEEE 35th Annual International Physics Symposium, 8–10 April 1997, Denver, CO, USA; pp. 141–148.
[17]  Semmens, J.E. Flip chips and acoustic micro imaging: An overview of past applications, present status, and roadmap for the future. Microelectron. Reliab. 2000, 40, 1539–1543.
[18]  Zhang, G.M.; Harvey, D.M.; Braden, D.R. Microelectronic package characterisation using scanning acoustic microscopy. NDT E Int. 2007, 40, 609–617.
[19]  Zhang, G.M.; Zhang, C.Z.; Harvey, D.M. Sparse signal representation and its applications in ultrasonic nde. Ultrasonics 2012, 52, 351–363.
[20]  Rojas, R. Neural networks—A systematic introduction; Springer-Verlag: Berlin, Germany, 1996.
[21]  Su, L.; Zha, Z.; Lu, X.; Shi, T.; Liao, G. Using bp network for ultrasonic inspection of flip chip solder joints. Mechan. Syst. Signal Process 2013, 34, 183–190.
[22]  Vapnik, V.N. The nature of Statistical Learning Theory; Springer: Berlin, Germany, 1995.
[23]  Yun, T.S.; Sim, K.J.; Kim, H.J. Support vector machine-based inspection of solder joints using circular illumination. Electron. Lett. 2000, 36, 949–951.
[24]  Zhang, Y.L.; Guo, N.; Du, H.; Li, W.H. Automated defect recognition of c-sam images in IC packaging using support vector machines. Int. J. Adv. Manuf. Tech. 2005, 25, 1191–1196.
[25]  Sonoscan. In C-SAM Operator Training, 1st ed. ed.; Sonoscan Inc.: Chicago, IL, USA, 2012; pp. 1–99.
[26]  Lewis, J.P. Fast normalized cross-correlation. Ind. Light Magic 1995, 1–7.
[27]  Ye, F.M.; Zhang, Z.B.; Chakrabarty, K.; Gu, X.L. Board-level functional fault diagnosis using artificial neural networks, support-vector machines, and weighted-majority voting. IEEE Tran. Comput. Aided Des. Integr. Circuit Syst. 2013, 32, 723–736.
[28]  Lin, C.-J. LIBSVM Tools. Available online: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/ (accessed on 25 October 2012).

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