A self-attentive physics-informed and data-driven framework for predicting solder joint reliability in printed circuit board assemblies under thermal aging

Reliable prediction of solder joint microstructure evolution and mechanical degradation under thermal aging is essential for ensuring long-term reliability of electronic assemblies. However, existing approaches often require large datasets or fail to capture complex microstructure-property interactions, limiting their predictive accuracy. Here, we introduce a self-attention physics-informed neural network (SA-PINN), which embeds an Arrhenius-based diffusion model into the loss function, integrates a transformer self-attention mechanism to capture complex interactions between microstructure and performance, and employs a Squeeze-and-Excitation block for dynamic channel recalibration. Trained on 240 samples spanning widely used package types aged at 94, 120, and 150 °C, SA-PINN simultaneously predicts intermetallic compound (IMC) growth, Pb-phase coarsening, and shear strength, achieving R² values of 0.992, 0.958, and 0.932, respectively, while reducing mean absolute error (MAE) by up to 50 % compared with strong data-driven regressors. Ablation studies confirm that both attention and channel re-weighting contribute synergistically to performance gains, and Shapley analyses quantify the main effects of microstructural and geometric descriptors on each target, consistent with experiments; meanwhile, the model-learned activation energies for IMC growth are broadly consistent with values fitted from Arrhenius plots, supporting physical consistency. This data-efficient yet physics-consistent framework offers high interpretability and can be readily extended to fatigue-life forecasting and multi-field coupling problems, thereby providing a versatile tool for rapid and reliable assessment as well as design guidance in electronic packaging.