Real time conditioning monitoring of MOSFET using artificial neural network regression

Abstract

The metal–oxide–semiconductor field-effect transistor (MOSFET) is a critical semiconductor device widely used in electrical and electronic systems. The performance of systems depends on the component’s reliability. More than 30% of failures of electronic and electrical systems are due to MOSFET failure. Industries are paying more apprehension towards the improvement of MOSFET reliability. Temperature stress is one of the key parameters to examine the reliability performance of semiconductor devices and to predict the lifetime of the components. This article provides a quick analysis of the MOSFET degradation models. The primary failure precursor metric that reflects the level of degradation is the MOSFET on-resistance R DS(on). In this paper, a linear, non-linear, and artificial neural network (ANN) deterioration model of MOSFETs is presented with the experimental data. The coefficient of determination (R-square), and root mean square error (RMSE) are used to compare the real data. Finally, it is demonstrated that the proposed model works well with experimental data. More than 500 data were trained for the proposed model. It can therefore predict the real-time conditioning monitoring of MOSFETs to increase the reliability of electrical systems.