A Novel Prediction Technology of Output Characteristics for IGBT Based on Compact Model and Artificial Neural Networks

The output characteristics of the insulated gate bipolar transistor (IGBT) are the critical metric for the measurement of power control and conversion of power electronic systems. Existing methods are characterized by potential issues, such as high cost and extremely low simulation efficiency. In this article, by combining compact models and artificial neural networks (ANNs), we propose a novel technique for predicting the static output characteristics of IGBTs. The proposed method can rapidly predict the Hefner static model parameters including performance parameters. By introducing the model parameters into the electronic design automation (EDA) circuit simulator, output characteristics can be obtained. In addition, a phased prediction (PP) scheme is proposed to further reduce the prediction error of the model parameters. The effectiveness of the method is verified by comparing the results of the proposed method with those in the technical computer-aided design (TCAD) simulation and datasheet. Meanwhile, the method significantly improves the speed compared with TCAD simulation, which can improve the efficiency of obtaining electrical characteristics and reduce the design cost.

Automated summary

In this article, a novel technique for predicting the static output characteristics of IGBTs is proposed by combining compact models and artificial neural networks (ANNs). The method utilizes model parameters in the electronic design automation (EDA) circuit simulator to obtain output characteristics, and a phased prediction (PP) scheme is introduced to reduce the prediction error. The effectiveness of the method is verified by comparing results with TCAD simulation and datasheet, and the method significantly improves speed and reduces design cost compared to TCAD simulation.