A Monte Carlo-Based Probabilistic Approach to Switching Loss Estimation in Power MOSFETs

Abstract

Switching losses have been shown to have a significant impact on the efficiency and thermal management of power electronic systems, particularly in high-performance converters. Conventional estimation techniques frequently rely on deterministic parameters, which are unable to account for the inherent variability in semiconductor characteristics and gate-driving conditions. This limitation can result in erroneous predictions and an underestimation of design margins. In order to address this issue, the present paper proposes a probabilistic approach for estimating switching losses in MOSFETs, with applications demonstrated for both Silicon carbide (SiC) and Silicon (Si) devices, using the Monte Carlo method. The methodology involves treating key device and driver parameters, such as gate resistance, transconductance, and threshold voltage, as statistical variables. This approach enables the capture of inherent uncertainties in device behavior. The switching transients are characterized through Double Pulse Test (DPT) simulations across a wide range of voltage and current levels. Monte Carlo simulations are extensively performed to derive the statistical distribution of energy losses, ensuring realistic performance expectations under variable conditions. The findings indicate that parameter variability can lead to substantial discrepancies in switching loss predictions, underscoring the limitations of conventional deterministic methods. The proposed methodology provides a more robust and reliable foundation for thermal design, loss prediction, and reliability assessment in power converter applications, ultimately ensuring improved performance and increased lifespan of power electronic systems.