Yuan Cadmus, Fan Xuejun, Zhang Gouqi
Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung 407082, Taiwan.
Department of Mechanical Engineering, Lamar University, Beaumont, TX 77710, USA.
Materials (Basel). 2021 Aug 26;14(17):4835. doi: 10.3390/ma14174835.
Solder joint fatigue is one of the critical failure modes in ball-grid array packaging. Because the reliability test is time-consuming and geometrical/material nonlinearities are required for the physics-driven model, the AI-assisted simulation framework is developed to establish the risk estimation capability against the design and process parameters. Due to the time-dependent and nonlinear characteristics of the solder joint fatigue failure, this research follows the AI-assisted simulation framework and builds the non-sequential artificial neural network (ANN) and sequential recurrent neural network (RNN) architectures. Both are investigated to understand their capability of abstracting the time-dependent solder joint fatigue knowledge from the dataset. Moreover, this research applies the genetic algorithm (GA) optimization to decrease the influence of the initial guessings, including the weightings and bias of the neural network architectures. In this research, two GA optimizers are developed, including the "back-to-original" and "progressing" ones. Moreover, we apply the principal component analysis (PCA) to the GA optimization results to obtain the PCA gene. The prediction error of all neural network models is within 0.15% under GA optimized PCA gene. There is no clear statistical evidence that RNN is better than ANN in the wafer level chip-scaled packaging (WLCSP) solder joint reliability risk estimation when the GA optimizer is applied to minimize the impact of the initial AI model. Hence, a stable optimization with a broad design domain can be realized by an ANN model with a faster training speed than RNN, even though solder fatigue is a time-dependent mechanical behavior.
焊点疲劳是球栅阵列封装中的关键失效模式之一。由于可靠性测试耗时，且物理驱动模型需要考虑几何/材料非线性，因此开发了人工智能辅助模拟框架，以建立针对设计和工艺参数的风险评估能力。鉴于焊点疲劳失效具有时间依赖性和非线性特征，本研究遵循人工智能辅助模拟框架，构建了非顺序人工神经网络（ANN）和顺序递归神经网络（RNN）架构。对这两种架构进行了研究，以了解它们从数据集中提取与时间相关的焊点疲劳知识的能力。此外，本研究应用遗传算法（GA）优化来减少初始猜测的影响，包括神经网络架构的权重和偏差。在本研究中，开发了两种GA优化器，包括“回归原始”和“递进”优化器。此外，我们将主成分分析（PCA）应用于GA优化结果，以获得PCA基因。在GA优化的PCA基因下，所有神经网络模型的预测误差均在0.15%以内。当应用GA优化器来最小化初始人工智能模型的影响时，在晶圆级芯片尺寸封装（WLCSP）焊点可靠性风险评估中，没有明确的统计证据表明RNN比ANN更好。因此，即使焊点疲劳是一种与时间相关的机械行为，通过训练速度比RNN更快的ANN模型也可以实现具有广泛设计域的稳定优化。
