A Bond Wire Aging Monitoring Method for IGBT Modules Based on Back Propagation Neural Networks

Abstract

A typical degradation mechanism of insulated gate bipolar transistor (IGBT) modules is the bond wire degradation (BWD), and thus the bond wire aging monitoring (AM) shows much attractiveness for IGBT modules. However, the performance degradation with junction temperature swings and load current dependence in many bond wire AM methods remains an obstacle. To address this, a bond wire AM method based on the back propagation neural networks (BPNN) is proposed in this paper, in which the on-state voltage drop (OVD) is used as the indicator of bond wire AM. In the proposed AM method, a multi-physical field coupling model of the IGBT module is established. Then, with the assistance of the model, the characterization behaviors of the OVD are thoroughly analyzed. According to the analysis, it is known that the junction temperature swings and load current dependence may obviously degrade the performance of the proposed AM method. Afterward, BPNN is adopted to deal with these issues. Finally, the performance of the proposed AM method is explored through extensive experimental tests.