Health Condition Assessment of Multi-Chip IGBT Module with Magnetic Flux Density

To achieve efficient conversion and flexible control of electronic energy, insulated gate bipolar transistor (IGBT) power modules as the dominant power semiconductor devices are increasingly applied in many areas such as electric drives, hybrid electric vehicles, railways, and renewable energy systems. It is known that IGBTs are the most vulnerable components in power converter systems. To achieve high power density and high current capability, several IGBT chips are connected in parallel as a multi-chip IGBT module, which makes the power modules less reliable due to a more complex structure. The lowered reliability of IGBT modules will not only cause safety problems but also increase operation costs due to the failure of IGBT modules. Therefore, the reliability of IGBTs is important for the overall system, especially in high power applications. To improve the reliability of IGBT modules, this thesis proposes a new health state assessment model with a more sensitive precursor parameter for multi-chip IGBT module that allows for condition-based maintenance and replacement prior to complete failure. Accurate health condition monitoring depends on the knowledge of failure mechanism and the selection of highly sensitive failure precursor. IGBT modules normally wear out and fail due to thermal cycling and operating environment. To enhance the understanding of the failure mechanism and the external characteristic performance of multi-chip IGBT modules, an electro-thermal finite element model (FEM) of a multi-chip IGBT module used in wind turbine converter systems was established with considerations for temperature dependence of material property, the thermal coupling effect between components, and the heat transfer process. The electro-thermal FEM accurately performed temperature distribution and the distribution electrical characteristic parameters during chip solder degradation. This study found an increased junction temperature, large change of temperature distribution, and more serious imbalanced current sharing during a single chip solder aging, thereby accelerating the aging of the whole IGBT module. According to the change of thermal and electrical parameters with chip solder fatigue, the sensitivity of fatigue sensitive parameters (FSPs) was analyzed. The collector current of the aging chip showed the highest sensitivity with the chip solder degradation compared with the junction temperature, case temperature, and collector-emitter voltage. However, the current distribution of internal components remains inaccessible through direct measurements or visual inspection due to the package. As the relationship between the current and magnetic field has been studied and gradually applied in sensor technologies, magnetic flux density was proposed instead of collector current as a new precursor for health condition monitoring. Magnetic flux density distribution was extracted by an electro-thermal-magnetic FEM of the multi-chip IGBT module based on electromagnetic theory. Simulation results showed that magnetic flux density had even higher sensitivity than collector current with chip solder degradation. In addition, the magnetic flux density was only related with the current and was not influenced by temperature, which suggested good selectivity. Therefore, the magnetic flux density was selected as the precursor due to its better sensitivity, selectivity, and generality. Finally, a health state assessment model based on backpropagation neural network (BPNN) was established according to the selected precursor. To localize and evaluate chip solder degradation, the health state of the IGBT module was determined by the magnetic flux density for each chip and the corresponding operating conduction current. BPNN featured good self-learning, self-adapting, robustness and generalization ability to deal with the nonlinear relationship between the four inputs and health state. Experimental results showed that the proposed model was accurate and effective. The health status of the IGBT modules was effectively recognized with an overall recognition rate of 99.8%. Therefore, the health state assessment model built in this thesis can accurately evaluate current health state of the IGBT module and support condition-based maintenance of the IGBT module

An analysis of the thermal interaction between components in power converter applications

Accurately predicting the temperature of semiconductor devices is very important in the initial design of power electronics converter. RC thermal models derived from well-known methods have some ability to predict the temperature. However, the accuracy is boundary condition specific, hence, these methods cannot be used in the reliability analysis. To make the thermal model more accurate and robust the factors contributing to discrepancies need to be analyzed carefully. These are power-module-materials’ non-linear properties, thermal grease layer and the cooling system (i.e., liquid-cooled cold plate). In this work, estimation of accurate RC parameters from FEA thermal model is demonstrated in COMSOL. The electrical model having temperature dependent power loss model is coupled to refined thermal model and solved in a circuit simulator, PLECS. The proposed method is applied in two applications: assessing thermal interaction between IGBTs and anti-parallel diodes in a half-bridge power module, and assessing thermal interaction among the discrete switches in an interleaved bidirectional DC-DC converter. Results show that the impact of material non-linearity, thermal grease layer and cooling boundary conditions are significant for accurate prediction of IGBT and diode temperatures. The proposed model is consistent to FEA results and differs by 2-6.5% comparing to the experimental results

In-service diagnostics for wire-bond lift-off and solder fatigue of power semiconductor packages

Wire-bond lift-off and Solder fatigue are degradation mechanisms that dominate the lifetime of power semiconductor packages. Although their lifetime is commonly estimated at the design stage, based on mission profiles and Physics-of-Failure models, there are many uncertainties associated with such lifetime estimates, emerging, for example, from model calibration errors, manufacturing tolerances, etc. These uncertainties, combined with the diverse working environments of power semiconductor packages result in inaccurate lifetime estimates. This paper presents an approach for estimating the extent of degradation in power semiconductor packages based on online monitoring of key parameters of the semiconductor, namely, the thermal resistance Rthja and the electrical resistance RCE. Using these two parameters, solder fatigue and wire-bond lift-off can be detected during normal converter operation. In order to estimate these two parameters, two techniques are introduced: a residual obtained from a Kalman filter which estimates the change in the thermal resistance Rthja and a Recursive Least-Squares (RLS) algorithm which is used to estimate the electrical resistance. Both methods are implemented online and validated experimentally

Reliability Assessment of IGBT through Modelling and Experimental Testing

Lifetime of power electronic devices, in particular those used for wind turbines, is short due to the generation of thermal stresses in their switching device e.g., IGBT particularly in the case of high switching frequency. This causes premature failure of the device leading to an unreliable performance in operation. Hence, appropriate thermal assessment and implementation of associated mitigation procedure are required to put in place in order to improve the reliability of the switching device. This paper presents two case studies to demonstrate the reliability assessment of IGBT. First, a new driving strategy for operating IGBT based power inverter module is proposed to mitigate wire-bond thermal stresses. The thermal stress is characterised using finite element modelling and validated by inverter operated under different wind speeds. High-speed thermal imaging camera and dSPACE system are used for real time measurements. Reliability of switching devices is determined based on thermoelectric (electrical and/or mechanical) stresses during operations and lifetime estimation. Second, machine learning based data-driven prognostic models are developed for predicting degradation behaviour of IGBT and determining remaining useful life using degradation raw data collected from accelerated aging tests under thermal overstress condition. The durations of various phases with increasing collector-emitter voltage are determined over the device lifetime. A data set of phase durations from several IGBTs is trained to develop Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models, which is used to predict remaining useful life (RUL) of IGBT. Results obtained from the presented case studies would pave the path for improving the reliability of IGBTs

Challenges and New Trends in Power Electronic Devices Reliability

The rapid increase in new power electronic devices and converters for electric transportation and smart grid technologies requires a deepanalysis of their component performances, considering all of the different environmental scenarios, overload conditions, and high stressoperations. Therefore, evaluation of the reliability and availability of these devices becomes fundamental both from technical and economicalpoints of view. The rapid evolution of technologies and the high reliability level offered by these components have shown that estimating reliability through the traditional approaches is difficult, as historical failure data and/or past observed scenarios demonstrate. With the aim topropose new approaches for the evaluation of reliability, in this book, eleven innovative contributions are collected, all focusedon the reliability assessment of power electronic devices and related components

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place