Embedding-Encoded Artificial Neural Network Model for MOSFET Preselection: Integrating Analytic Loss Models with Dynamic Characteristics from Datasheets

The rapid emergence of wide-bandgap devices, such as SiC and GaN, has greatly expanded the choices of components. Efforts have been devoted to developing analytical models for calculating the power losses. However, the complexity arises due to the dependence of power losses on several variables and operating conditions, making the computation intensive and challenging when comparing hundreds of components. To address these limitations and expedite power loss estimation for a wide range of component choices and operating conditions, this paper proposes the application of an embedding encoding Artificial Neural Network (ANN) for power MOSFETs loss modelling. The ANN models the power losses with the dynamic parameters from datasheets under the required operational conditions. In the meantime, embedding encoding is implemented to integrate the discrete category information into the ANN. Furthermore, the structure of the proposed embedding ANN is optimized to reach a low training, validating and testing losses to ensure the regression accuracy. Experimental validation on a SiC device confirms the efficacy of the proposed ANN model for estimating power losses on MOSFETs. This research accelerates the preselection of MOSFETs, benefiting the design automation of power electronics.