2026-04-07T10:03:49Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/69292025-03-29T07:15:27Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Villalobos Cano, Adrian author Barrutia, Iban author Peña Alzola, Rafael author Dragicevic, Tomislav author Aizpurua, José I. author 2025 Semiconductor devices, especially MOSFETs (Metal–oxide–semiconductor field-effect transistor), are crucial in power electronics, but their reliability is affected by ageing processes influenced by cycling and temperature. The primary ageing 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 ageing 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. 1873-6769 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=180405 https://hdl.handle.net/20.500.11984/6929 semiconductors Forecasting condition monitoring Temporal Fusion Transformers neural networks Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring