Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring
This work addresses reliability issues in power electronics for engineers and manufacturers, but it is incremental as it compares existing methods with a novel application of TFTs.
The research tackled the challenge of forecasting aging in semiconductor devices like MOSFETs, comparing methods including classical tracking, statistical forecasting, neural networks, and Temporal Fusion Transformers (TFTs), with TFTs achieving valid outcomes for long-term predictions by integrating future covariates and identifying key aging turning points.
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.