Condition monitoring for solder layer degradation in multi-device system based on neural network

Abstract

Power semiconductor devices (chips) are usually arranged in parallel to increase the power rating of the modules for high power applications like renewable energy. In multi-device systems uneven degradation of the devices is inevitable. The uneven solder layer degradation of the parallel chips translates into higher thermal resistances for the degraded chips and, according to the electrothermal properties of the devices, the current sharing and temperature distribution between the devices will be affected. This phenomenon will have implications on the global reliability of the power module. In this study, a two-stage neural network (NN) approach is proposed for the diagnosis of the degradation: the first stage NN estimates the power losses of the parallel devices, whose deviations from the reference values are then applied to the NN in the second stage to classify the health condition. This condition monitoring method has been evaluated in on-state experiments at different constant current values, indicating that it could be a suitable strategy for improving the operational reliability of converters employing multi-chip power modules.

Keywords

• multichip modules
• reliability
• failure analysis
• neural nets
• temperature distribution
• power semiconductor devices
• thermal resistance
• condition monitoring
• solders
• multichip power modules
• multidevice system
• power semiconductor devices
• power rating
• high power applications
• renewable energy
• multidevice systems uneven degradation
• uneven solder layer degradation
• parallel chips translates
• higher thermal resistances
• degraded chips
• current sharing
• temperature distribution
• power module
• two-stage neural network approach
• stage NN
• power losses
• parallel devices
• health condition
• condition monitoring method
• different constant current values