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

A COMPARISON BETWEEN DATA-DRIVEN AND PHYSICS OF FAILURE PHM APPROACHES FOR SOLDER JOINT FATIGUE

Prognostics and systems health management technology is an enabling discipline of technologies and methods with the potential of solving reliability problems that have been manifested due to complexities in design, manufacturing, environmental and operational use conditions, and maintenance. Over the past decade, research has been conducted in PHM to provide benefits such as advance warning of failures, enable forecasted maintenance, improve system qualification, extend system life, and diagnose intermittent failures that can lead to field failure returns exhibiting no-fault-found symptoms. While there are various methods to perform prognostics, including model-based and data-driven methods, these methods have some key disadvantages. This thesis presents a fusion prognostics approach, which combines or ―fuses together‖ the model based and data-driven approaches, to enable increasingly better estimates of remaining useful life. A case study using an electronics system to illustrate a step by step implementation of the fusion approach is also presented. The various benefits of the fusion approach and suggestions for future work are included

HEALTH ESTIMATION AND REMAINING USEFUL LIFE PREDICTION OF ELECTRONIC CIRCUIT WITH A PARAMETRIC FAULT

Degradation of electronic components is typically accompanied by a deviation in their electrical parameters from their initial values. Such parametric drifts in turn will cause degradation in performance of the circuit they are part of, eventually leading to function failure due to parametric faults. The existing approaches for predicting failures resulting from electronic component parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. However, in practical applications where the components are integrated into a complex electronic circuit assembly, product or system, it is generally not feasible to monitor component-level parameters. To address this problem, a prognostics method that exploits features extracted from responses of circuit-comprising components exhibiting parametric faults is developed in this dissertation. The developed prognostic method constitutes a circuit health estimation step followed by a degradation modeling and remaining useful life (RUL) prediction step. First, the circuit health estimation method was developed using a kernel-based machine learning technique that exploits features that are extracted from responses of circuit-comprising components exhibiting parametric faults, instead of the component-level parameters. The performance of kernel learning technique depends on the automatic adaptation of hyperparameters (i.e., regularization and kernel parameters) to the learning features. Thus, to achieve high accuracy in health estimation the developed method also includes an optimization method that employs a penalized likelihood function along with a stochastic filtering technique for automatic adaptation of hyperparameters. Second, the prediction of circuit’s RUL is realized by a model-based filtering method that relies on a first principles-based model and a stochastic filtering technique. The first principles-based model describes the degradation in circuit health with progression of parametric fault in a circuit component. The stochastic filtering technique on the other hand is used to first solve a joint ‘circuit health state—parametric fault’ estimation problem, followed by prediction problem in which the estimated ‘circuit health state—parametric fault’ is propagated forward in time to predict RUL. Evaluations of the data from simulation experiments on a benchmark Sallen–Key filter circuit and a DC–DC converter system demonstrate the ability of the developed prognostic method to estimate circuit health and predict RUL without having to monitor the individual component parameters

PROGNOSTIC MODELING FOR RELIABILITY PREDICTIONS OF POWER ELECTRONIC DEVICES

The applications of semiconductor power electronic devices, including power and RF devices, in industry have stringent requirements on their reliability. Power devices are subject to various types of failure mechanisms under various stressors. Prognostics and health management (PHM) allows detecting signs of failures, providing warnings of failures in advance, and performing condition-based maintenance. There is a pressing need to develop a robust prognostic model to detect anomalous behavior and predict the lifetime of devices that can be applicable to different types of power transistors. In the present dissertation, a comprehensive prognostic model for remaining useful life (RUL) prediction of semiconductor power electronic devices is developed. The model consists of an anomaly detection module and a RUL prediction module including a non-linear system process model describing the evolution of parametric degradation, and a measurement model. The anomaly detection module uses principal component analysis (PCA) for dimensionality reduction and feature extraction, as well as k-means clustering to establish baseline clusters in the feature space. The novel singular-value-weighted distance (SVWD) is developed as the distance measure in the feature space, based on which Fisher criterion (FC) is used for anomaly probability calculation. The system process model incorporates variables concerning loading conditions and physics-of-failure of devices, and uses particle filter (PF) approach for process model training and RUL prediction. For PF methodology, a novel resampling technique, called MHA-replacement resampling, is developed to solve the particle degeneracy in classic PF techniques and sample impoverishment in traditional resampling techniques. The developed prognostic model is first implemented on IGBT modules for validation. It was reported that the module package of power transistors was susceptible to various types of fatigue-related failure modes due to coefficient of thermal expansion (CTE) mismatches under temperature/power cycles introducing thermomechanical stresses. The physics-of-failure "driving variable" is derived from Paris equation. The model is validated on several time-series IGBT module degradation data under power cycles from literature sources, based on SIR particle filter for RUL prediction with good accuracy. Then the model is implemented on GaN HEMTs, a representative of wide-bandgap semiconductor power devices. GaN HEMTs are susceptible to degradation mechanisms such as ohmic contact inter-diffusion that leads to voiding in the field plate at high temperature under RF accelerated life tests (ALTs). The time-series data of the physics-of-failure "driving variable" is obtained from diffusion computation in Mathematica with the temperature prole coming from COMSOL thermal simulation. The RUL prediction results based on SIR lter are also satisfactory for GaN HEMTs. For each type of device, the new resampling technique is validated through performance benchmarking against state-of-the-art resampling techniques. Another reliability threat for GaN HEMTs, especially in aerospace and nuclear applications, is the degradation due to radiation effect on the device performance. Gamma radiation has been found to lead to generation of defects in AlGaN/GaN layers, which form complexes acting as carrier traps, thus reducing carrier density and current. EPC GaN HEMTs are irradiated under a wide range of Gamma ray doses and critical DC characteristics are recorded before and after radiation to quantify their shifts during the irradiation. Future work needed to allow implementation of the developed prognostic model for RUL estimation is proposed

A wavelet-fuzzy based algorithm for condition monitoring and fault detection of a voltage source inverter.

Abstract Not Provided

Feature Papers in Electronic Materials Section

This book entitled "Feature Papers in Electronic Materials Section" is a collection of selected papers recently published on the journal Materials, focusing on the latest advances in electronic materials and devices in different fields (e.g., power- and high-frequency electronics, optoelectronic devices, detectors, etc.). In the first part of the book, many articles are dedicated to wide band gap semiconductors (e.g., SiC, GaN, Ga2O3, diamond), focusing on the current relevant materials and devices technology issues. The second part of the book is a miscellaneous of other electronics materials for various applications, including two-dimensional materials for optoelectronic and high-frequency devices. Finally, some recent advances in materials and flexible sensors for bioelectronics and medical applications are presented at the end of the book

Modeling and Simulation in Engineering

The general aim of this book is to present selected chapters of the following types: chapters with more focus on modeling with some necessary simulation details and chapters with less focus on modeling but with more simulation details. This book contains eleven chapters divided into two sections: Modeling in Continuum Mechanics and Modeling in Electronics and Engineering. We hope our book entitled "Modeling and Simulation in Engineering - Selected Problems" will serve as a useful reference to students, scientists, and engineers