Real-Time Nonlinear Behavioral Electrothermal Device-Level Emulation of IGBT on Heterogeneous Adaptive Compute Acceleration Platform

Abstract

Power converter design evaluation by means of real-time simulation techniques is prevalent, although it is mostly restricted to simple power semiconductor switch models that exclude device-level physical details. In this work, the nonlinear high-order electrothermal model of the IGBT is developed and then deployed onto the heterogeneous digital hardware for real-time implementation. As the complexity of the NBM of the IGBT poses a significant computational burden on real-time hardware emulation, ML methodology is utilized so that the trained model can reproduce the characteristics of its original counterpart as much as possible and then it is implemented on the ACAP, which composes of the PS, PL, and AIE. The vector multiplication feature of the AIE caters to mathematical operations of the ML-based model particularly well and consequently enables it to be executed in real-time with remarkable speedup over the original model with which matrix inversion is otherwise mandatory. Finally, the validation for real-time device-level results and system-level results of a multiconverter system is provided by SaberRD and MATLAB/Simulink.

Keywords

• Adaptive compute acceleration platform (ACAP)
• AI engine (AIE)
• artificial neural network (ANN)
• field-programmable gate array (FPGA)
• insulated-gate bipolar transistor (IGBT)
• machine learning (ML)