IGBT Junction Temperature Estimation Based on Machine Learning Method
Jigui Miao, Quan Yin, Hao Wang, Yang Liu, Haichun Li, Sanding Duan
Abstract
In IGBT power module, nearly 55% of the decrease of device reliability is caused by the increase of junction temperature. IGBT junction temperature estimation is a useful way to minimize failure rate. In this paper, three nonlinear machine learning-based models including Support Vector Regression (SVR), Random Forest (RF) and Back-Propagation Neural Network (BPNN) are constructed to automatically estimate the junction temperature under different parameter values. The simulation results are compared and prove that this machine learning method can be used and have a good prospect of industrial application.
Topics & Concepts
Junction temperatureInsulated-gate bipolar transistorSupport vector machineReliability (semiconductor)Artificial neural networkComputer sciencePower (physics)Temperature measurementMachine learningArtificial intelligenceRandom forestEngineeringElectrical engineeringVoltageQuantum mechanicsPhysicsSilicon Carbide Semiconductor TechnologiesThin-Film Transistor TechnologiesElectrostatic Discharge in Electronics