| Summary: | This article combines the sequential artificial neural network (NN) machine learning with finite element (FE) modeling to assess the solder joint thermal cycling performance. A glass wafer-level chip-scale package (G-WLCSP) is used for this study. This article investigates the network structure that can achieve prediction capability both inside and outside the design domain with the minimal required training dataset. First, a detailed FE model for G-WLCSP is developed to obtain the accumulated plastic strain per cycle for thermal-cycling loading. Three critical input parameters are defined to generate a dataset based on finite element analysis. Then, applying the supervised machine learning procedure, both the recurrent neural network (RNN) and the gate-network long short-term memory (LSTM) architecture are used to train the obtained dataset. The network complexity of the sequential NN model is carefully controlled to prevent numerical overfitting. Among the total 81 FE generated data pairs, only 27 data pairs have been applied to the sequential NN learning. These 27 data pairs are carefully selected to evenly distributed among the design domain. The average error norms after the learning are 1.213 · 10<sup>-4</sup> and 1.190 · 10<sup>-4</sup> of RNN and LSTM, respectively. The prediction capability of the well-trained sequential NN model against the rest 54 data pairs has been tested and a similar scale has been obtained. Furthermore, the prediction capability is tested against the parameters outside the design domain. Approximately one order average error norm increased for both the well-trained RNN and LSTM model.
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