IGBT Research Articles

IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

ABSTRACTThe insulated gate bipolar transistor (IGBT) is one of the most important power semiconductor devices in power electronics and is also prone to failure. High junction temperature and junction temperature fluctuation of IGBT are the main causes of IGBT module aging failure. The high‐precision monitoring of the junction temperature of the IGBT module is a prerequisite for IGBT life prediction, which is crucial for reducing maintenance costs and improving equipment reliability. Therefore, an IGBT junction temperature estimation method based on long short‐term memory (LSTM) neural network and sliding window estimation model is proposed and applied in practical industrial scenarios. This method uses the operating data of the motor drive device in the actual industrial application scenario as the training and test data set and uses the external operating parameters of the IGBT module to estimate the junction temperature of the IGBT module. Compared with the internal operating parameters of the IGBT module based on switching transient, the external operating parameters are easier to collect and process, and more suitable for practical application scenes. A sliding window estimation model is proposed to estimate the junction temperature of the IGBT module. Compared with the point‐to‐point estimation method, the sliding window estimation method can capture the influence of historical operation data better and has a higher capability of time series data estimation. The IGBT junction temperature estimation of sliding windows is realized by the LSTM neural network, which is more suitable for time series estimation in real industrial scenarios. The experimental results show that the estimation accuracy of the sliding window estimation method is better than that of the point‐to‐point estimation method, and the accuracy of the sliding window estimation method based on LSTM is better than that of the sliding window estimation method based on other machine learning models. It proves that the proposed method can better capture the dynamic process of the system and has higher estimation accuracy.

Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings

This study investigates the feasibility of using fiber Bragg grating (FBG) sensors for multipoint thermal monitoring of several power semiconductor devices (PSDs), such as insulated gate bipolar transistors (IGBTs), and rectifiers assembled on a common heatsink in a three-phase inverter. A novel approach is proposed to integrate FBG sensors beneath the baseplates of the IGBT modules, avoiding the need for invasive modifications to the device structure. By strategically positioning multiple FBG sensors, accurate temperature profiles of critical components can be obtained. The experimental results demonstrate the effectiveness of the proposed method, with the temperature measurements from FBG sensors closely matching those obtained using thermal infrared (IR) cameras within ±1.1 °C. This research highlights the potential of FBG sensors for reliable and precise thermal management in power electronic systems, contributing to improved performance and reliability.

Investigation of a fast gate driver based on a half-turn planar transformer.

This paper presents a magnetically isolated gate driver for the fast switching of IGBT (Insulated Gate Bipolar Transistor) in compact pulsed power sources with sharp rising edges and flat-top pulses for the application of electromagnetic launch and food processing. The proposed gate driver is implemented based on a planar transformer with a half-turn winding arrangement to increase the amplitude of the driving voltage pulse. With the half-turn winding arrangement, the leakage inductance of transformers decreases by 31.1% compared to the interleaving structure. This decrease enables a fast rise time of the driving voltage pulse. Furthermore, the gate drivers are used to drive the IGBT switching a Blumlein pulse forming network. The results show that the di/dt of the applied commercially available Si IGBT is about 10.10 A/ns with a gate voltage of 50V and a gate capacitance charging time of about 88ns, proving the effectiveness of the gate driver and providing a high-performance driving scheme for the fast switching of Si IGBT.

Remaining useful life prediction with spatio-temporal graph transform and weakly supervised adversarial network: An application in power components

Remaining useful life (RUL) prediction is crucial in improving the reliability of insulated gate bipolar transistors (IGBTs) and has attracted widespread attention all over the world. Owing to the heterogeneity and complex internal connectivity of the IGBTs, accurate RUL prediction by traditional deep learning (DL) methods remains challenging. Herein, a novel prognostic framework is proposed for their RUL prediction. In brief, the non-smooth spike voltage of IGBTs is transformed into a graph matrix that contains rich correlation information. Then, spatio-temporal fusion graph network (STFGN) is proposed to realize RUL prediction. It contains spatial-scale and temporal-scale extractors, where the former one based on GraphSage can obtain unique IGBTs data structure information, while the latter based on bidirectional long short-term memory (BiLSTM) captures the time-series degradation features of IGBTs. Moreover, the weakly supervised adversarial learning strategy is considered to realize cross-domain prediction using scarce labeled data, which enhance the positive transfer. Four transfer tasks under variable working conditions are considered to prove the proposed method. The experimental results demonstrate that the average SF and RMSE metrics of the proposed method are 0.429 and 0.108, which is superior to other related methods.

Evaluating a novel bidirectional soft-switching DC-DC converter for electric vehicles

This research aims to build unique zero voltage transition (ZVT) non-isolated bidirectional DC-DC converters for hybrid electric vehicle battery storage. First, a high-voltage gain bidirectional converter (BDC) is examined. This converter can soft-switch insulated gate bipolar transistors (IGBTs). The primary insulated-gate bipolar transistors (IGBTs) are operated under zero-current conditions throughout the turn-on to turn-off commutation phase to reduce switching losses and increase efficiency. A soft-switched cell with a resonant inductor, capacitor, and additional IGBTs achieves zero-current turn-off. A new converter uses insulated-gate bipolar transistors with zero-voltage transition operation. Soft-switched cells improve the hard-switched bridgeless DC-DC converter (BDC). Resonant inductors, capacitors, and auxiliary switching devices make up the soft-switched cell. Soft-switched cells enable zero voltage turn-on of primary insulated-gate bipolar transistors. This converter charges the battery in buck mode and boosts it to provide the necessary output voltage. This study examined a 70 V/300 V power system's high-gain bidirectional converter (BDC) design simulation. The converter was tested at 50 kHz with 800 W output power. The high-gain soft-switched BDC has 96.5% boost and 97% buck efficiency. Operating principles, design analysis, and simulation assessments are included in this study.

IGBT Overcurrent Capabilities in Resonant Circuits

The control of IGBT (insulated gate bipolar transistor) and MOSFET (metal oxide semiconductor field effect transistor) is of great interest nowadays as they are widely used in electric vehicles, photovoltaic applications, and a multitude of systems. The field of power electronics and their correct activation ensures that the transistors are operated without being destroyed. In this work, a double resonant transformer was built and used to produce very high currents. These currents are switched by a full bridge of resonant IGBT transistors to demonstrate the feasibility of exceeding the maximum permissible transistor currents in a resonant system. The system is controlled by the feedback from two current sensors. In this case the currents exceed in a 170% the peak current of the transistor without problems. In this way, resonant circuits with IGBT transistors can be designed with currents lower than the maximum currents of the resonant circuit, therefore reducing the cost of the circuit and reducing the switching losses to nearly zero.

Enhanced flow boiling by manipulating two-phase flow in Tesla channel heat sink using HFE-7100

Flow boiling of dielectric fluids in copper microchannel heat sinks is highly desirable for cooling large-sized insulated gate bipolar transistor (IGBT) power electronic modules. However, dielectric fluids present challenges in flow boiling because of their unfavorable thermophysical properties. These factors make it difficult to enhance critical heat flux (CHF) without precooling. To address this, we developed a large copper heat sink (10 cm × 5 cm) with Tesla microchannels designed to suppress vapor backflow and promote intense fluid mixing. The microchannels have a high length to hydrodynamic diameter ratio of approximately 220, significantly higher than those in previous studies. Flow boiling experiments using HFE-7100 were conducted for both Tesla and plain-wall microchannels. Tesla channels demonstrated a 26.2 % increase in CHF and a 120 % improvement in heat transfer coefficient (HTC). These enhancements are attributed to the vapor backflow suppression and improved fluid mixing. Moreover, the standard deviation of wall temperature in plain-wall microchannels was 10 times higher than in Tesla channels, highlighting the effectiveness of the periodic Tesla valves in reducing two-phase flow instabilities. Flow pattern visualization was conducted to further understand the mechanism behind vapor regulation, clarifying the role of Tesla valves in controlling vapor backflow. This study demonstrates the potential of dielectric fluids in Tesla microchannels for flow boiling applications, offering a promising solution for cooling large electronics.

A Multiscale Adaptive Fusion Network for Modular Multilevel Converter Fault Diagnosis

Modular Multilevel Converters (MMCs) play a crucial role in new energy grid connection and renewable energy conversion systems due to the significant merits of good modularity, flexible scalability, and lower operating loss. However, reliability is a significant challenge for MMCs, which consist of a large number of Insulated Gate Bipolar Transistors (IGBTs). Failures of the IGBTs in submodules (SMs) are a critical issue that affect the performance and operation of MMCs. The insufficient ability of convolutional neural networks to learn key fault features affects the accuracy of MMC fault diagnosis. To resolve this issue, this paper proposes a novel deep fault diagnosis framework named the Multiscale Adaptive Fusion Network (MSAFN) for MMC fault diagnosis. In the proposed MSAFN, the fault features of the raw current in an MMC are extracted by employing multiscale convolutional neural networks (CNNs) firstly, and then a channel attention mechanism is added to adaptively select the channel containing key features, so as to improve the fault diagnosis ability of the MMC in a noisy environment. Finally, the adaptive size of a one-dimensional CNN is adopted to adjust the weight of the feature channels of different scales, which are adaptively fused for fault diagnosis. Experimental validation is performed on two different MMC datasets. Experimental results confirm that the introduction of an attention mechanism of the multiscale feature adaptive fusion channel improves the recognition accuracy of the model by an average of 15.6%. Moreover, comparative experiments under different signal-to-noise ratios (SNRs) demonstrate that the MSAFN maintains accuracy levels above 96.7%, highlighting its excellent performance, particularly under noisy conditions.

A Remaining Useful Life Prediction Method for Insulated-Gate Bipolar Transistor Based on Deep Fusion of Nonlinear Features From Multisource Data

A Remaining Useful Life Prediction Method for Insulated-Gate Bipolar Transistor Based on Deep Fusion of Nonlinear Features From Multisource Data

Modeling Thermal Impedance of IGBT Devices Based on Fractional Calculus Techniques

The thermal impedance characteristics of insulated gate bipolar transistor (IGBT) modules are critical for the thermal management and design of electronic devices. This paper proposes a fractional-order equivalent thermal impedance model, which is inspired by the correlation between multi-time-scale dissipation characteristics of heat conduction processes and fractional calculus. The fractional-order equivalent thermal impedance model is derived based on the connection between fractional-order calculus and the Foster thermal network model in mathematical operations, with only two parameters to be identified: heat capacity C and fractional order α. Moreover, this paper provides a parameter identification method for the proposed fractional-order equivalent thermal impedance model based on the multi-objective particle swarm optimization (MOPSO) algorithm. In order to validate the effectiveness and superiority of this work, experiments and comparative works are provided in this paper. The results indicate that the fractional-order equivalent thermal impedance model can accurately describe the frequency domain characteristic curves of the thermal impedance of the Foster thermal network model for IGBT modules, with the difference between the amplitude frequency characteristics not exceeding 1 dB and the difference between the phase frequency characteristics not exceeding 1° within the operating frequency range of (1 kHz, 1 MHz).

A novel structure of less switching loss IGBT with super junction

In order to further reduce the switching loss (ESW), a novel floating CS layer and super-junction insulated gate bipolar transistor (FCS-SJBJ-IGBT) with internal barrier junction is proposed and simulated. On the premise of not affecting breakdown voltage (BV), the CS layer of FCS-SJBJ-IGBT is floating, so that the P-body is connected with the P-drift region, and a small part of P-pillar is formed between the CS layer and the left gate, which enables electrons to flow rapidly through this region in the on state, and provides an extraction hole path in the off state to reduce the loss. The simulation results show that，under the same forward conduction voltage (VON) condition, the turn-on energy loss (EON) of FCS-SJBJ-IGBT is significantly lower than that of the traditional super-junction IGBT (con-SJ-IGBT) and 34.5 % lower than that of the recently reported super-junction IGBT (SJBJ-IGBT) with blocking junction, P-pillar and N-pillar, while their turn-off energy loss (EOFF) is almost the same. The research results can provide important reference for reducing the ESW of IGBT.

Research on Fault Prediction of Power Devices in Rod Control Power Cabinets Based on BiTCN-Attention Transfer Learning Model.

The Insulated Gate Bipolar Transistor (IGBT) is the key power device in the rod control power cabinet of nuclear power plants; its reliable operation is of great significance for ensuring the safe and economical operation of the nuclear power plants. Therefore, it is necessary to conduct fault prediction research on IGBT to achieve better condition-based maintenance and improve its operational reliability. However, power cabinets often operate under multiple, complex working conditions, so predicting IGBT faults from single working condition data usually has limitations and low accuracy. Its failure probability has an important relationship with the actual operating conditions of the cabinet. In order to improve the reliability and maintainability of the control power cabinet in nuclear power plants, this paper takes IGBTs in the rod control power cabinet as the object and makes full use of the data of IGBTs under multiple working conditions to carry out research on the cross-condition fault prediction of IGBTs under multiple-source working conditions. A transfer learning (TL) model based on a bidirectional time convolutional network (BiTCN) combined with attention was proposed to solve the problem of low accuracy of cross-operating fault prediction in a multi-source domain. Firstly, an IGBT fault simulation model was built to collect the life cycle state data of the module under different working conditions. Then, after pre-processing such as removing outliers, kernel principal component analysis (KPCA) was used to integrate all source domain data, obtain source domain characterization data, and train the BiTCN-attention model. Finally, the BiTCN-attention model trained in the source domain was transferred, and the model was fine-tuned according to the target domain data. Simulation results show that the accuracy of the proposed BiTCN-attention transfer learning prediction method can reach more than 99%, which is significantly better than that of the recurrent neural network transfer learning (RNN-TL) model, long short-term memory network transfer learning (LSTM-TL) model, gated cyclic unit transfer learning (GRU-TL) model, and time convolutional network transfer learning (TCN-TL) model. This method can not only reduce the inconsistency of fault characteristic values caused by changes in working conditions but also accurately predict the degradation trend when only early fault data are available, providing an effective solution for IGBT fault prediction across working conditions in multi-source domains.

Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults. This paper provides a comprehensive overview of the fundamental principles of multi-level inverters and the various sorts of faults that can occur in multi-level inverters. This study provides a comprehensive analysis of five-level cascaded H-bridge multilevel inverters (MLIs) under both normal and defective conditions. The paper outlines a fault-detection method that utilizes total harmonic distortion and a normalized output voltage factor. In addition, the paper discusses a fault-isolation strategy that relies on reducing amplitude modulation. This method leads to the development of a fault-tolerant inverter. The utilization of level-shifted pulse-width modulation (LSPWM) technology is employed for the purpose of switching operations. LSPWM is the most appropriate technique for MLIs that require a low amount of computational resources. The fault-diagnosis approach given is suitable for MLI-based drives, grid-connected operations, and other applications. This paper presents a comprehensive examination of the 5L-CMLI (5-Level Cascaded Multi-Level Inverter) under various fault scenarios in CMLI. Subsequently, various fault diagnosis approaches will be examined, including their advantages and disadvantages. The paper discusses several defects that can occur in the Insulated Gate Bipolar Transistor (IGBT) of a Current Mode Logic Inverter (CMLI), and also presents a design for a reliable fault diagnosis system. Furthermore, this analysis examines several fault detection strategies in CMLI, categorized according to open-loop and closed-loop dynamic systems fault classifications.

Influence of different vibration directions on the solder layer fatigue in IGBT modules

Insulated-gate bipolar transistor (IGBT) modules are extensively utilized in high-speed trains, ships, and electric vehicles. Compared to those used in power systems, IGBT modules in these applications are more susceptible to vibration effects on their reliability. This paper proposes a multi-physics field simulation method and a lifetime model for IGBT modules to assess the impact of different vibration directions on solder layer fatigue. Initially, a multi-physics field model of the IGBT module is developed, incorporating electrical, thermal, mechanical, and vibration coupling. The effectiveness of this multi-physics simulation model is verified by an experimental platform. Subsequently, the influence of different vibration directions on solder layer fatigue in the IGBT module is analysed, and a life model of the IGBT is proposed through simulation. Finally, a power cycling with a vibration environment experimental platform is established to validate the effect of vibration on solder layer fatigue in the IGBT module. The simulation and experimental results indicate that vertical vibration accelerates the solder layer fatigue of IGBT modules, and the lifetime of an IGBT module operating under vertical vibration at 30 Hz is about 15 % shorter than that of an IGBT module operating under power cycling alone. The error between the calculated results of the solder layer failure and the experimental result is <5 %.

Modelling of Power Semiconductor Devices Switching and Conduction Losses in a Power Electronic System

In power converters, power semiconductor devices, mainly Insulated-Gate Bipolar Transistor (IGBT), are generally used as they are less costly and capable of converting electrical energy into high frequency and voltage. It is critical to accurately determine the switching and conduction losses of power devices before they are assembled into an electronic system. The accurate values help to minimize power loss in a power converter system. With the aid of simulation, design problems are identified to help eliminate device destruction, and critical parameters are monitored. In addition, costly equipment for measuring purposes and testing prototypes is not required at the initial design stage. This research uses simulation work in a power converter system to predict IGBT and diode losses with varied frequencies and voltages. The predictive modelling is produced using regression analysis. The models have been validated by R square (R2) and Mean Absolute Percentage Error (MAPE) techniques. This work is aimed at providing a supervised learning technique mainly used in a machine learning environment, in line with the technological development in the semiconductor industry. With the simulation performed in this work, the efficiency of a boost converter system is enhanced as it shows the models have a good fit with an R2 value of almost 1, and MAPE values are noticed to be less than 5%.

Simulation study of a novel GaN on Si Quasi‐Vertical Reverse‐Conducting Insulated Gate Bipolar Transistor

Abstract In this paper, a novel GaN on Si quasi-vertical reverse conducting insulated gate bipolar transistor(RC-IGBT) with a new NPN structure is proposed and simulated by Silvaco TCAD. The distinctive feature of this structure lies in fabricating the original bottom P-Collector on the wafer surface. In comparison to conventional IGBT devices with PNPN structures, this NPN structure overcomes two major challenges: the difficulty in forming ohmic contacts due to the activation difficulty of the bottom P-GaN and the necessity of creating backside through-holes for electrode formation. Simultaneously, the N-GaN in the emitter and reverse-conducting regions can be selectively regrown through Metal-Organic Chemical Vapor Deposition (MOCVD) or Molecular Beam Epitaxy (MBE), avoiding the etching effect of top-layer N-GaN on P-GaN during the fabrication of P-type electrodes. This approach demonstrates a high level of process feasibility. In this paper, Silvaco TCAD is used to simulate the static and dynamic characteristics of this novel quasi-vertical RC-IGBT device. The simulation results reveal that the device has a high saturation current(Ion,sat) density of 18224.67A/cm2 at Vge=14V,a threshold voltage (Vth) of 5V,a breakdown voltage of 828V,a latch-up voltage of 800V ,and a switching speed of nanoseconds. In addition, the selection of device parameters is studied in this paper.&amp;#xD;

Research on Underwater Constant High-Voltage DC Switching Technology for MCSEM.

The marine controlled source electromagnetic (MCSEM) transmitter can transmit high currents near the seabed to detect the electrical structure of the seafloor. The use of three-phase alternating current (AC) transmission can lead to three-phase imbalance, which results in an excessive current in one phase's power line and affects the safety of the tow cable. This paper proposes an MCSEM underwater constant high-voltage direct-current (DC) switching scheme that replaces AC transmission with DC transmission. This scheme can fundamentally avoid three-phase imbalance and the AC loss caused by inductance. After establishing a simulation model to analyze the effect of the scheme, the relevant hardware units were designed. The hardware unit mainly consists of three parts: a DC switching inverter unit, a filter unit, and a step-down rectification unit. The DC inverter unit controls six insulated gate bipolar transistor (IGBT) modules with sinusoidal pulse width modulation (SPWM) signals to convert DC to three-phase AC power; the filter unit filters out extra harmonic components; and the step-down rectification unit converts high-voltage three-phase AC to low-voltage DC. The scheme ultimately achieved an adjustable DC output of 48.3-73.4 V under a constant DC input voltage of 3000 V and effectively reduced the current on the cable. This scheme has the potential to replace the previous AC transmission, reducing the risk of tow cable burnout and enhancing the safety of MCSEM operations.

An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks

The precise estimation of the operational lifespan of insulated gate bipolar transistors (IGBT) holds paramount significance for ensuring the efficient and uncompromised safety of industrial equipment. However, numerous methodologies and models currently employed for this purpose often fall short of delivering highly accurate predictions. The analytical approach that combines the Pattern Optimization Algorithm (POA) with Successive Variational Mode Decomposition (SVMD) and Bidirectional Long Short-term Memory (BiLSTM) network is introduced. Firstly, SVMD is employed as an unsupervised feature learning method to partition the data into intrinsic modal functions (IMFs), which are used to eliminate noise and preserve the essential signal. Secondly, the BiLSTM network is integrated for supervised learning purposes, enabling the prediction of the decomposed sequence. Additionally, the hyperparameters of BiLSTM and the penalty coefficients of SVMD are optimized utilizing the POA technique. Subsequently, the various modal functions are predicted utilizing the trained prediction model, and the individual mode predictions are subsequently aggregated to yield the model’s definitive final life prediction. Through case studies involving IGBT aging datasets, the optimal prediction model was formulated and its lifespan prediction capability was validated. The superiority of the proposed method is demonstrated by comparing it with benchmark models and other state-of-the-art methods.

Reliability Simulation of IGBT Module with Different Solders Based on the Finite Element Method

The interconnecting solder is a key control factor for the reliability of electronic power packaging because it highly affects the junction temperature of insulated-gate bipolar transistor (IGBT) modules and is prone to plasticity, creep, and other failure behaviors under temperature-change environments. In this paper, the interconnecting performance and fatigue life of five different kinds of solders such as SAC305, sintered silver, Au80Sn20, sintered copper, and pure In under direct current (DC), power cycle, and electro-thermal coupling complex environments were studied based on electro-thermal multi-physical field coupling finite element simulation method, respectively. Results show that the sintered silver owns the most outstanding thermal reliability and the DC operating junction temperature of the IGBT module after utilizing sintered silver solder is only 90.2 °C, which is nearly 15 °C lower than that of the IGBT module utilizing SAC305 solder. Furthermore, in the power cycle reliability test, the fatigue life of Au80Sn20 solder reaches a maximum of 3.26 × 107 cycles while the life of indium presents only 5.85 × 103 cycles, a difference of nearly four orders of magnitude. Finally, under the complex environment of electro-thermal coupling, the fatigue life of Au80Sn20 solder is also the largest at 1.9 × 106 cycles, while the smallest life of solder becomes SAC305 solder at 4.44 × 102 cycles. The results of this paper can provide a theoretical basis for solder selection and life prediction of the IGBT module, which is of great significance in improving the reliability of power electronic packaging.

Dynamic numerical calculation of multi-physics fields in IGBT chip layer based on ETM-PINN

The electric-thermal-mechanical (ETM) multi-physics coupling fields commonly exists in power electronic devices such as insulate-gate bipolar transistor (IGBT), which dominates the evolution of key state variables inside the components. Among them, the junction e and electric potential of the chip layer, as the dependent variables of various lifetime physical models, directly affect its remaining service life and health status. Aiming at the problem that the key physical variables inside the IGBT are difficult to monitor and predict, this paper first integrates and establishes the ETM coupling partial differential equations (PDEs) inside the IGBT, then accordingly proposes an accurate numerical calculation method for coupled physical fields based on ETM-physics-informed neural networks (ETM-PINN).Through relevant simulation verification, it can not only realize the numerical calculation of the chip layer junction temperature and electric potential field without data, but also perform multi-physics predictive calculation with reduced accuracy in the absence of the key coefficients in PDEs. In the presence of additional physical field data, it can also perform data-model fusion calculations to further improve the solution accuracy.