Using BP network for ultrasonic inspection of flip chip solder joints

Abstract Flip-chip technology has been used extensively in microelectronic packaging, where defect inspection for solder joints plays an extremely important role. In this paper, ultrasonic inspection, one of the non-destructive methods, was used for inspection of flip chip solder joints. The image of the flip chip was captured by scanning acoustic microscope and segmented based on the flip chip structure information. Then a back-propagation network was adopted, and the geometric features extracted from the image were fed to the network for classification and recognition. The results demonstrate the high recognition rate and feasibility of the approach. Therefore, this approach has high potentiality for solder joint defect inspection in flip chip packaging.

[1]  Shih-Chieh Chen,et al.  Irregular shapes classification by back-propagation neural networks , 2007 .

[2]  F. Voci,et al.  Fuzzy inference filter and morphological operators for short circuits detection in printed circuit board , 2002, Industrial Electronics, 2002. ISIE 2002. Proceedings of the 2002 IEEE International Symposium on.

[3]  Cheng-Lung Huang,et al.  Defect spatial pattern recognition using a hybrid SOM-SVM approach in semiconductor manufacturing , 2009, Expert Syst. Appl..

[4]  Peter W. Tse,et al.  Classification of gear faults using cumulants and the radial basis function network , 2004 .

[5]  F. Guo.  Composite lead-free electronic solders , 2006 .

[6]  S. Brand,et al.  Automated inspection and classification of flip-chip-contacts using scanning acoustic microscopy , 2010, Microelectron. Reliab..

[7]  Alexander Sassov,et al.  X-ray digital microlaminography for BGA and flip-chip inspection , 2000 .

[8]  Hyung Suck Cho,et al.  A neural network approach to the inspection of ball grid array solder joints on printed circuit boards , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[9]  Augusto Montisci,et al.  Neural networks based AOI systems for electronic devices diagnosis , 2003, International Commission for Optics.

[10]  Tielin Shi,et al.  A novel approach for flip chip solder joint inspection based on pulsed phase thermography , 2011 .

[11]  Chao-Ton Su,et al.  A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process , 2009 .

[12]  A.N. Belbachir,et al.  An automatic optical inspection system for the diagnosis of printed circuits based on neural networks , 2005, Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005..

[13]  Sheng Liu,et al.  Defects pattern recognition for flip chip solder joint quality inspection with laser ultrasound and interferometer , 2002, 52nd Electronic Components and Technology Conference 2002. (Cat. No.02CH37345).

[14]  Y. C. Chan,et al.  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 , 2008 .

[15]  James Hensman,et al.  Natural computing for mechanical systems research: A tutorial overview , 2011 .

[16]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[17]  T. C. Chai,et al.  A novel defect detection technique using active transient thermography for high density package and interconnections , 2003, 53rd Electronic Components and Technology Conference, 2003. Proceedings..

[18]  Giuseppe Acciani,et al.  Application of neural networks in optical inspection and classification of solder joints in surface mount technology , 2006, IEEE Transactions on Industrial Informatics.

[19]  Giuseppe Acciani,et al.  A Neurofuzzy Method for the Evaluation of Soldering Global Quality Index , 2009, IEEE Transactions on Industrial Informatics.