Numerical modeling of FS-trench IGBTs by TCAD and its parameter extraction method

Abstract

The electrical behavior of the IGBTs is the key factors to the application of IGBTs. Technology Computer Aided Design (TCAD) is one of the most effective methods for characterizing of electrical behavior of IGBT. However, the great difficulties in extracting the model's parameters hinder its widely applications. In the present work, a high-precision TCAD model for FS-Trench IGBT was established and verified by experiments. Firstly, the parameters necessary for TCAD models were analyzed in theoretical and experimental. The key process parameters need to be determined in a value range table. A Convolutional Neural Network (CNN) was proposed and trained to form a relationship from characteristic curves to key process parameters. Subsequently, the numerical model's parameters were established by combining the trained CNN model with experimental data. Finally, the TCAD model was established and the simulations of IGBTs were carried out and verified by experiments. Results demonstrate that the proposed TCAD model is well consistent with experiments with a Root Mean Square Error (RMSE) of about 3.7 %. This work may provide a feasible method to obtain highly precise numerical models of power semiconductor devices. It may also promote the application of Artificial Intelligence in the device modeling and optimization design.