Solder joint reliability predictions using physics-informed machine learning

The reliability of solder joints plays an increasingly important role in power electronics. The thermal fatigue experienced due to the temperature fluctuations cause catastrophic failures. However, the ability to predict the fatigue for different thermal cycles is lacking. Experimental or simulation based approaches are typically too expensive to be conducted for a wide range of thermal loading conditions. A physics informed Long Short-Term Memory (PI-LSTM) is proposed here for predicting the plastic strain and related fatigue lifetime in solder joints. The LSTM model is trained on data generated by FEM simulations, enhanced by incorporating the flow rule into the loss function. The PI-LSTM accurately predicts the plastic strain and the stress components, enabling efficient reliability predictions. Using different reliability models, the estimated cycles to failure are found to be in close agreement with those from conventional FEM simulations, demonstrating the PI-LSTM’s capability for reliability assessments.