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Thermo-mechanical fatigue reliability optimization of PBGA solder joints based on ANN-PSO

  • Ji-cheng Zhou (周继承)Email author
  • Xiao-qing Xiao (肖小清)
  • Yun-fei En (恩云飞)
  • Ni Chen (陈 妮)
  • Xiang-zhong Wang (王湘中)
Article

Abstract

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 words

thermo-mechanical fatigue reliability solder joints plastic ball grid array finite element analysis Taguchi method artificial neural network particle swarm optimization 

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Copyright information

© Central South University Press and Springer-Verlag GmbH 2008

Authors and Affiliations

  • Ji-cheng Zhou (周继承)
    • 1
    • 2
    Email author
  • Xiao-qing Xiao (肖小清)
    • 2
    • 3
  • Yun-fei En (恩云飞)
    • 3
  • Ni Chen (陈 妮)
    • 2
  • Xiang-zhong Wang (王湘中)
    • 1
  1. 1.School of Electronics and Information EngineeringCentral South University of Forestry and TechnologyChangshaChina
  2. 2.School of Physics Science and TechnologyCentral South UniversityChangshaChina
  3. 3.China Ceprei LaboratoryGuangzhouChina

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