Ultra-Fast Power Module Inductance Estimation using Convolutional Neural Networks

The widespread usage of wide bandgap (WBG) semiconductors forces extra emphasis on the early estimation of the layout parasitic elements. Be it a printed circuit board or a power module, layout optimization is necessary to minimize the negative effects of present inductances. Unfortunately, multiple invocations of inductance extraction software can be time-consuming. In this work, state-of-the-art convolutional neural networks (CNN) are applied in order to lower the time consumption of inductance estimation without compromising the accuracy

Calorimetric measurement methodology for comprehensive soft and hard switching loss characterisation

Accurate measurement of soft and hard switching losses is challenging. Electrical methods are prone to errors and calorimetric measurements most often cannot separate turn-on and turn-off energies. We present a calorimetric test setup capable of measuring and separating turn-on and turn-off energies in soft and hard switching regimes. The resulting loss map can be used to accurately predict power semiconductor losses, even when the converter is not fully in the soft-switching regime