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Solder Joint Reliability Risk Estimation by AI-Assisted Simulation Framework with Genetic Algorithm to Optimize the

Cadmus Yuan1, Xuejun Fan2, Gouqi Zhang3

  • 1Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung 407082, Taiwan.

Materials (Basel, Switzerland)
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Summary

Artificial neural networks (ANN) and recurrent neural networks (RNN) were evaluated for solder joint fatigue risk estimation. Genetic algorithm optimization showed ANN models offer stable, fast training for reliable ball-grid array packaging, even with time-dependent fatigue.

Keywords:
artificial neural networkgeneric algorithmprinciple component analysisrecurrent neural networksolder joint fatigue risk estimationtime/temperature-dependent nonlinearitywafer level chip-scaled packaging

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Area of Science:

  • Materials Science and Engineering
  • Artificial Intelligence in Engineering
  • Reliability Engineering

Background:

  • Solder joint fatigue is a critical failure mode in ball-grid array (BGA) packaging.
  • Traditional reliability testing is time-consuming, and physics-driven models require complex nonlinearities.
  • AI-assisted simulation offers a potential solution for risk estimation against design and process parameters.

Purpose of the Study:

  • To develop and evaluate an AI-assisted simulation framework for solder joint fatigue risk estimation.
  • To compare the effectiveness of artificial neural network (ANN) and recurrent neural network (RNN) architectures in capturing time-dependent fatigue behavior.
  • To optimize neural network performance using genetic algorithm (GA) and principal component analysis (PCA).

Main Methods:

  • Development of non-sequential ANN and sequential RNN architectures within an AI-assisted simulation framework.
  • Application of two genetic algorithm (GA) optimizers ('back-to-original' and 'progressing') to tune neural network initializations.
  • Utilizing principal component analysis (PCA) on GA optimization results to derive a PCA gene for model refinement.

Main Results:

  • All neural network models achieved a prediction error within 0.15% when using the GA-optimized PCA gene.
  • No significant statistical advantage of RNN over ANN was found for wafer-level chip-scaled packaging (WLCSP) solder joint reliability risk estimation with GA optimization.
  • ANN models demonstrated faster training speeds compared to RNN models.

Conclusions:

  • ANN models provide a stable and efficient approach for solder joint reliability risk estimation in BGA packaging.
  • GA optimization effectively minimizes the impact of initial conditions on AI models, leading to high prediction accuracy.
  • ANNs are a viable alternative to RNNs for time-dependent solder fatigue analysis, offering computational advantages.