2023 journal article

Sequence Prediction for SiC MOSFET Active Gate Driving With a Recurrent Neural Network

IEEE OPEN JOURNAL OF INDUSTRY APPLICATIONS, 4, 227–237.

By: L. Yang  n, Y. Liu, W. Yu n  & I. Husain n

author keywords: Mathematical models; Switches; Integrated circuit modeling; MOSFET; Data models; Logic gates; Recurrent neural networks; Active gate driver (AGD); deep learning; recurrent neural network (RNN); sequence prediction; SiC MOSFET
TL;DR: The AGD sequences generated by the predictor effectively and precisely improve the switching transients, making the proposed sequence predictor an integral and valuable component for active gate driving. (via Semantic Scholar)
Source: Web Of Science
Added: January 8, 2024

This article develops a recurrent neural network (RNN) with an encoder–decoder structure to predict the driving sequence of SiC MOSFET active gate drivers (AGDs). With a set of switching targets as the input, the predictor generates an optimal active gate driving sequence to improve the switching transient. The development is based on a hybrid platform across MATLAB, PyTorch, and LTspice. A high-fidelity switching model is implemented in MATLAB to obtain reliable training data. The sequence predictor is trained with PyTorch. The predicted sequence is verified on an example Buck circuit in LTspice. In contrast to the state-of-the-art approach, the proposed method avoids exhaustive search in a large solution space; the sequence length is dynamically predicted per the driving strength at each step. The AGD sequences generated by the predictor effectively and precisely improve the switching transients, making the proposed sequence predictor an integral and valuable component for active gate driving.