Thermo-mechanical fatigue reliability optimization of PBGA solder joints based on ANN-PSO
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Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The simulation experiments of accelerated thermal cycling test were performed by ANSYS software. Based on orthogonal array experiments, a back-propagation artificial neural network (BPNN) was used to establish the nonlinear multivariate relationship between thermo-mechanical fatigue reliability and control factors. Then, PSO was applied to obtaining the optimal levels of control factors by using the output of BPNN as the affinity measure. The results show that the control factors, such as print circuit board (PCB) size, PCB thickness, substrate size, substrate thickness, PCB coefficient of thermal expansion (CTE), substrate CTE, silicon die CTE, and solder joint CTE, have a great influence on thermo-mechanical fatigue reliability of PBGA solder joints. The ratio of signal to noise of ANN-PSO method is 51.77 dB and its error is 33.3% less than that of Taguchi method. Moreover, the running time of ANN-PSO method is only 2% of that of the BPNN. These conclusions are verified by the confirmative experiments.
Key wordsthermo-mechanical fatigue reliability solder joints plastic ball grid array finite element analysis Taguchi method artificial neural network particle swarm optimization
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- SYED A. Predicting solder joint reliability for thermal, power, and bend cycle within 25% accuracy [C]// Proc 51st Electronic Components and Technology Conference. Orlando: IEEE Press, 2001: 255–263.Google Scholar
- HUANG Chun-yue, ZHOU De-jian, WU Zhao-hua. Solder joint reliability of plastic ball grid array component based on design of full factorial experiment [J]. Journal of Xi’an Jiaotong University, 2005, 39(7): 753–756. (in Chinese)Google Scholar
- IPC-9701. Performance test methods and qualification reqirements for surface mount solder attachments [S].Google Scholar
- HU Shou-ren. Application techniques for artificial neural networks [M]. Changsha: National University of Defense Technology Press, 1993. (in Chinese)Google Scholar
- ZHOU Ji-cheng, XIAO Xiao-qing, EN Yun-fei, HE Xiao-qi. Optimal design for improving thermo-mechanical fatigue reliability of solder joint of PBGA component based on robust design [J]. Acta Electronica Sinica, 2007, 35(11): 2180–2183. (in Chinese)Google Scholar