Reliability of Power Electronic Modules

Abstract

The electric car, the all electric aircraft and requirements for renewable energy are examples of potential technologies needed to address the world problem of global warming/carbon emission etc. Power electronics and packaged modules are fundamental for the underpinning of these technologies and with the diverse requirements for electrical configurations and the range of environmental conditions, time to market is paramount for module manufacturers and systems designers alike. This paper details some of the results from a major UK project into the reliability of power electronic modules using physics of failure techniques. This paper presents a design methodology together with results that demonstrate enhanced product design with improved reliability, performance and value within acceptable time scales.

Application of Kriging and radial basis function in power electronic module wire bond structure reliability under various amplitude loading

“…Conchodal fracture results in an instant catastrophic failure of the model. This conchodal fracture failure has been modelled using FEA by Lu et al [7]. Another approach using probability calculation with FEA to predict the failure of conchodal fracture is reported in [8].…”

Evaluation of Fatigue Life of Semiconductor Power Device by Power Cycle Test and Thermal Cycle Test Using Finite Element Analysis

“…In order to drive a motor efficiently, direct current must be converted to alternating current by an inverter. The electrical device used in the inverter is called the power device [1,2]. Currently, the power devices in a car is used as an auxiliary for the internal-combustion engine.…”

Application of coupled electro-thermal and physics-of-failure-based analysis to the design of accelerated life tests for power modules

“…A number of approaches in the literature [23,24,25] have been used for prognosis of health management of power devices which include data driven techniques and physics-of-failure techniques [26,27,28,29]. In data driven techniques the current and historic information is used to statistically and probabilistically derive estimates of the remaining useful life for the product [30,31,32].…”