Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring
Villalobos, Adrian and Barrutia, Iban and Peña-Alzola, Rafael and Dragicevic, Tomislav and Aizpurua, Jose I. (2025) Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring. Engineering Applications of Artificial Intelligence, 150. 110545. ISSN 0952-1976 (https://doi.org/10.1016/j.engappai.2025.110545)
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Abstract
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.
ORCID iDs
Villalobos, Adrian, Barrutia, Iban, Peña-Alzola, Rafael
ORCID: https://orcid.org/0000-0002-2451-6779, Dragicevic, Tomislav and Aizpurua, Jose I.;
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Item type: Article ID code: 93937 Dates: DateEvent15 June 2025Published20 March 2025Published Online7 March 2025AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Humanities and Social Sciences (HaSS) > Psychological Sciences and HealthDepositing user: Pure Administrator Date deposited: 26 Aug 2025 08:55 Last modified: 14 Aug 2026 02:08 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/93937
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