Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model

Due to the disparate timescale behavior in the electrical and thermal aspects, achieving a balance between simulation efficiency and accuracy in electrothermal analysis of insulated gate bipolar transistor (IGBT) modules has been a challenging task. A physical-based electrothermal stress evaluation approach combining with artificial neural network (ANN) model is proposed in this article, which significantly improves performance in circuit simulation. The training data for ANN models are derived from the Hefner physical model, a well-established model integrated in Saber. By re-expressing the Hefner model using MATLAB scripts, high-precision data can be efficiently obtained. Double-pulse experiments show that the switching transient characterized by the Hefner model have high precision, with an error within 5% compared to the experimental data. Additionally, the transient behavior of IGBT devices is further described by a two-layer feed-forward ANN, trained using datasets obtained by varying parasitic or operating parameters in the re-expressed Hefner model. Combining the physical model with the ANN models, the proposed approach can simulate not only transient electrical behavior but also long-term thermal behavior with accurate switching energy. This approach has been implemented in MATLAB/Simulink and verified with Saber for system-level circuit simulation. The electrothermal stress evaluation results show that the simulation efficiency is significantly improved (180 times faster than Saber under the simulation settings in this article), while maintaining high precision, and the error is within 2.5%. Experimental results also validate the accuracy of proposed model in predicting the voltage and current stress, with a maximum error of 1.5%.