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This paper proposes a computationally efficient methodology to predict thedamage progression in solder contacts of electronic components usingtemperature-time curves. For this purpose, two machine learning algorithms, aMultilayer Perceptron and a Long Short-Term Memory network, are trained andcompared with respect to their prediction accuracy and the required amount oftraining data. The training is performed using synthetic, normally distributeddata that is realistic for automotive applications. A finite element model of asimple bipolar chip resistor in surface mount technology configuration is usedto numerically compute the synthetic data. As a result, both machine learningalgorithms show a relevant accuracy for the prediction of accumulated creepstrains. With a training data length of 350 hours (12.5% of the availabletraining data), both models show a constantly good fitting performance of $R^2$of 0.72 for the Multilayer Perceptron and $R^2$ of 0.87 for the Long Short-TermMemory network. The prediction errors of the accumulated creep strains are lessthan 10% with an amount of 350 hours training data and decreases to less than 5% when using further data. Therefore, both approaches are promising for thelifetime prediction directly on the electronic device.

Performance Assessment of different Machine Learning Algorithm for Life-Time Prediction of Solder Joints based on Synthetic Data