An Accurate and Intelligent Approach to Predicting the Power Device Fatigue Failure Process

It is significant to study power device package fatigue failure as it seriously affects the reliability of power system. Nevertheless, the research of power device failure process is insufficient. In this paper, an accurate and intelligent approach is proposed to predict the power device fatigue failure process with multiple fatigue sampling method (MFSM) and minimal component unit method (MCUM). MFSM is proposed to accurately build the power device lifetime model. It is accomplished through multiple sampling fatigue morphology evolution process of solder layers combined with the fatigue parameter. Morphology evolution is detected by scanning acoustic microscope (SAM) technology under accelerated lifetime test (ALT). The fatigue parameter is got through finite element analysis (FEA) by establishing each sampling geometry model. Then, the lifetime model is determined by their same failure area fraction ( F _s ). In particular, digital image processing (DIP) is applied to detailly describe solder layer shapes which is also the key to building a real FEA geometry model. MCUM is utilized to complete the prediction of failure process, where solder layers are divided into minimal units and the FEA solution and location information of each unit are known. Based on lifetime model, the failure area can be got and the fatigue failure process can be finished intelligently by cosimulation. The proposed method is accurate and intelligent enough in predicting the failure of solder layers which is more helpful for planned device management.