Failure Mode Classification of IGBT Modules Under Power Cycling Tests Based on Data-Driven Machine Learning Framework

Of great significance is knowledge of failure modes of IGBT modules under power cycling test (PCT) in advance. It can not only precisely determine accurate utilization of physics-of-failure lifetime prediction methods but also help optimize IGBT designs. However, establishing an accurate and generic offline failure mode classification method for different IGBT modules remains a challenging problem due to the complex degradation process of IGBT modules. In this article, a data-driven convolutional neural network (CNN) based method is proposed for quad-classification of failure modes for different IGBT modules under different PCT conditions. First, in order to accurately characterize the mapping between the failure modes and the precursor parameters, a framework of precursor parameters is meticulously established. Then, mainly using collected training data from existing publications, the CNN classification model is developed by a newly dynamic tuning-multilevel particle swarm-back propagation optimization algorithm. Finally, experimental PCTs of various IGBT modules are performed. The obtained PCT data and additional testing data from existing publications are used to verify the generalizability and robustness of the proposed classification method. The superiority of the proposed method is well demonstrated through comparison with random CNN, the state-of-the-art particle swarm optimization CNN, and other intelligent algorithms.