Semiconductor devices, especially MOSFETs (Metal-oxide-semiconductor field-effect transistor), are crucial in power electronics, but their reliability is affected by aging processes influenced by cycling and temperature. The primary aging mechanism in discrete semiconductors and power modules is the bond wire lift-off, caused by crack growth due to thermal fatigue. The process is empirically characterized by exponential growth and an abrupt end of life, making long-term aging forecasts challenging. This research presents a comprehensive comparative assessment of different forecasting methods for MOSFET failure forecasting applications. Classical tracking, statistical forecasting and Neural Network (NN) based forecasting models are implemented along with novel Temporal Fusion Transformers (TFTs). A comprehensive comparison is performed assessing their MOSFET ageing forecasting ability for different forecasting horizons. For short-term predictions, all algorithms result in acceptable results, with the best results produced by classical NN forecasting models at the expense of higher computations. For long-term forecasting, only the TFT is able to produce valid outcomes owing to the ability to integrate covariates from the expected future conditions. Additionally, TFT attention points identify key ageing turning points, which indicate new failure modes or accelerated ageing phases.

翻译：半导体器件，尤其是MOSFET（金属氧化物半导体场效应晶体管），在电力电子领域至关重要，但其可靠性受循环和温度影响的老化过程所制约。分立半导体与功率模块的主要老化机制是键合线剥离，由热疲劳引起的裂纹扩展所致。该过程在经验上表现为指数增长和寿命的突然终止，使得长期老化预测具有挑战性。本研究对MOSFET故障预测应用中的不同预测方法进行了全面的比较评估。经典跟踪法、统计预测法以及基于神经网络（NN）的预测模型与新型时序融合Transformer（TFT）一同被实现。通过评估它们在不同预测时间跨度下的MOSFET老化预测能力，进行了全面比较。对于短期预测，所有算法均能获得可接受的结果，其中经典NN预测模型以更高计算量为代价取得了最佳效果。对于长期预测，仅TFT能够产生有效结果，这得益于其整合来自预期未来条件协变量的能力。此外，TFT注意力点可识别关键的老化转折点，这些转折点指示了新的失效模式或加速老化阶段。