LSTM combined with attention mechanism for power MOSFET lifetime prediction

Power metal oxide semiconductor field-effect transistors (MOSFET) is a key component of the circuits and electronic systems. This paper addresses the critical challenge of predicting the lifetime of MOSFET in smart meters, which are prone to degradation due to electrical and thermal stress. Traditional methods often fall short in handling the dynamic nature of component aging. To overcome these limitations, we propose an innovative approach that leverages Long Short-Term Memory (LSTM) networks enhanced with an attention mechanism. By processing operational data from MOSFET, the model dynamically adapts to changes in the data, ensuring more accurate predictions. The approach is further validated and optimized using accelerated aging experiments, offering a robust solution for improving the reliability and longevity of MOSFET components in practical applications.