Feasibility of quasi-square-wave zero-voltage-switching bi-directional dc/dc converters with gan hemts

There are trade-offs for each power converter design which are mainly dictated by the switching component and passive component ratings. Recent power electronic devices such as Gallium Nitride (GaN) transistors can improve the application range of power converter topologies with lower conduction and switching losses. These new capabilities brought by the GaN High Electron Mobility Transistors (HEMTs) inevitably changes the feasible operation ranges of power converters. This paper investigates the feasibility of Buck and Boost based bi-directional DC/DC converter which utilizes Quasi-Square-Wave (QSW) Zero Voltage Switching (ZVS) on GaN HEMTs. The proposed converter applies a high-switching frequency at high output power to maximize the power density at the cost of high current ripple with high frequency of operation which requires a design strategy for the passive components. An inductor design methodology is performed to operate at 28 APP with a switching frequency of 450 kHz. In order to minimize the high ripple current stress on the output capacitors an interleaving is performed. Finally, the proposed bi-directional converter is operated at 5.4 kW with 5.24 kW/L or 85.9 W/in3 volumetric power density with air-forced cooling. The converter performance is verified for buck and boost modes and full load efficiencies are recorded as 97.7% and 98.7%, respectively

Özgan transistörlerin karakterizasyonu ve paralel anahtarlar için kısa devre korumasına sahip yarım köprü ile çift yönlü da/da çevirici geliştirilmesi

Wide band-gap semiconductors are superior to Si-based semiconductors with their increased electron mobility and breakdown strength which leads to small package sizes, low parasitics, and increased switching frequency capability. Efficient and dense power converter could be obtained with wide band-gap devices especially GaN transistors. This thesis investigates the GaN HEMTs in terms of their characterization and pplication. The gate charge and output capacitance of GaN HEMTs are characterized by designed experimental setups and they are compared with manufacturer-provided data. The differences between outcomes and the datasheet are highlighted and explained. Based on the characterized elements of GaN HEMTs, their switching performance is studied within a simulation platform and effects of package capacitances, parasitic inductances, temperature, gate resistance are discussed. A half-bridge prototype design is performed where layout optimization is done for a parallel-connected GaN HEMTs. Moreover, a short circuit protection technique is implemented on the same half-bridge board to save GaN HEMTs from overcurrent and increase the reliability. The short circuit protection method is able to detect the fault in 40 ns and can control the short circuit current in 100 ns. Lastly, an example application is realized with GaN HEMT based half-bridges to have a bi-directional DC/DC converter. The bi-directional DC/DC converter has 5.4 kW power rating with 5.24 kW/l power density. This power density is achieved with 450 kHz of switching frequency where zero voltage switching is applied with critical conduction mode switching. The efficiency of the converter is 97.7% at maximum load.---M.S. - Master of Scienc

ANALYSING DEPTH DATA WITH SUPERVISED AND UNSUPERVISED LEARNING

reservedThis thesis investigates the application of machine learning algorithms for distance determination in the context of smart glasses technology. The primary objective is to enhance visual assistance capabilities by accurately predicting distance values, particularly focal length adjustments, based on sensor data. The study explores various machine learning methods, including k-means clustering, K-nearest neighbors (KNN) regression, Convolutional Neural Network (CNN) and DBSCAN clustering, to analyze their effectiveness in distance segmentation and prediction. The research begins with an exploration of adjustable lens technology and vision impairment concepts to establish foundational knowledge. Subsequently, machine learning algorithms are applied to extract meaningful insights from distance data collected by Time-of-Flight (ToF) sensors. Supervised and unsupervised learning techniques are employed for distance segmentation, enabling precise categorisation of depth measurements into meaningful clusters. Key findings from the study demonstrate the effectiveness of machine learning methods in accurately determining distance values, which is crucial for optimizing visual experiences and enhancing user interaction with smart glasses. Additionally, domain knowledge is integrated into the clustering process to inform k-selection and validate cluster consistency.This thesis investigates the application of machine learning algorithms for distance determination in the context of smart glasses technology. The primary objective is to enhance visual assistance capabilities by accurately predicting distance values, particularly focal length adjustments, based on sensor data. The study explores various machine learning methods, including k-means clustering, K-nearest neighbors (KNN) regression, Convolutional Neural Network (CNN) and DBSCAN clustering, to analyze their effectiveness in distance segmentation and prediction. The research begins with an exploration of adjustable lens technology and vision impairment concepts to establish foundational knowledge. Subsequently, machine learning algorithms are applied to extract meaningful insights from distance data collected by Time-of-Flight (ToF) sensors. Supervised and unsupervised learning techniques are employed for distance segmentation, enabling precise categorisation of depth measurements into meaningful clusters. Key findings from the study demonstrate the effectiveness of machine learning methods in accurately determining distance values, which is crucial for optimizing visual experiences and enhancing user interaction with smart glasses. Additionally, domain knowledge is integrated into the clustering process to inform k-selection and validate cluster consistency

Investigation of Turn-on and Turn-off Characteristics

In this paper, turn-on and turn-off switching behavior of 650V enhancement-mode GaN power FETs are investigated. An analytical model is developed to analyze the current-voltage characteristics of the device during switching transients both with and without the effects of parasitic components. In addition, the effect of the temperature and circuit parameters on the switching characteristics are investigated

Layout Based Ultra-Fast Short-Circuit Protection Technique for Parallel Connected GaN HEMTs

Gallium Nitride Enhancement-Mode High Electron Mobility Transistors (GaN HEMTs) help to achieve high power density converter circuits thanks to their superior efficiency, higher switching speed and small package size. However, increased switching speed results in a sharp increase in short circuit (SC) current under a shoot-through fault with respect to other type of devices. GaN HEMTs can withstand the SC current only for several hundred nanoseconds. Therefore, fast SC protection solutions are critical for protecting power circuits. In this paper, the voltage induced by high slew rate of SC current on the high frequency power loop inductance resulting from the printed circuit board (PCB) layout is sensed to implement an ultra-fast short-circuit protection technique. The proposed technique does not increase circuit parasitics and provides flexibility in layout design that makes it suitable for parallel connected GaN HEMTs, which require symmetric layout design for equal current sharing. A multi-pulse test is conducted under 1.56 MHz switching frequency, 400 VDC bus voltage and 40 A load current by using parallel connected GaN HEMTs in a half-bridge configuration to verify the robustness and reliability of the proposed protection technique. Experimental results show that the proposed protection technique is able to detect SC fault within 40 ns and fault is completely cleared with a soft turn-off in 250 ns

Investigation of Turn-on and Turn-off Characteristics of Enhancement-Mode GaN Power Transistors

In this paper, turn-on and turn-off switching behavior of 650V enhancement-mode GaN power FETs are investigated. An analytical model is developed to analyze the current-voltage characteristics of the device during switching transients both with and without the effects of parasitic components. In addition, the effect of the temperature and circuit parameters on the switching characteristics are investigated

PCB Layout Based Short-Circuit Protection Scheme for GaN HEMTs

Gallium Nitride Enhancement-Mode High Electron Mobility Transistors (GaN HEMTs) are superior to other power transistors in terms of efficiency, package size and switching speed which leads to increased power density in power converter applications. However, GaN HEMTs have much shorter short-circuit withstand time compared to the conventional devices, which is limited to several hundred nanoseconds. Therefore, reliable and fast protection solutions are required to protect GaN HEMTs from fatal over-current failures. In this paper, a novel short-circuit (SC) protection scheme based on fault current sensing by using Printed Circuit Board (PCB) layout parasitics is proposed. The proposed scheme uses the voltage drop on the parasitic inductance of the PCB trace to detect very intense high slew rate SC faults. In addition, the voltage drop on the parasitic resistance of the PCB trace is utilized to detect relatively slow over-current (OC) faults. Once a fault is detected, a soft turn-off mechanism is initiated by the proposed circuit to turn-off devices gradually to eliminate over-voltage breakdown risk. The proposed circuit is verified by both SPICE simulations and hardware implementation. The experimental results show that both SC and OC faults can be detected and GaN HEMTs can be protected. The total operation duration for the circuit is 370 ns during a SC fault. The SC fault can be detected within 30 ns and the soft turn-off mechanism is initiated within 80 ns to terminate the SC current flowing through the GaN HEMTs within 290 ns

Investigation of the larger scale tungsten production by the electrochemical reduction technique

Hydrogen reduction of WO3 is the major industrial process in tungsten production. A promising cost and energy efficient method was recently reported [1-3] for direct electrochemical production of tungsten from CaWO4 (scheelite) which is estimated as the two third of the all tungsten reserves of the world. Following the above mentioned patent, several studies verified the production of metallic tungsten by electrochemical reduction of calcium tungstate in the laboratory. This study investigates the application of electrochemical reduction technique to produce larger amount of metallic tungsten as compared to laboratory experiments. The electrolyte was composed of calcium chloride and sodium chloride salts at eutectic composition (48 % mol NaCl at 873 K). The reduced samples were cleaned with dilute hydrochloric acid solutions and characterized by XRD and SEM. Examination of the results revealed that there are several problems against pilot-scale production, the biggest of which was observed as the electronic conduction provided by the graphite powders dispersed in the molten salt

Sclerosing pneumocytoma: two case reports and review of the literature

Abstract Not Availabl