Georgian Technical University Integrated Sensors For Direct Control.

GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power with integrated transistors, gate drivers, diodes and current and temperature sensors for condition monitoring. A team of Georgian Technical University researchers has succeeded in significantly enhancing the functionality of GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power for voltage converters: the researchers at Georgian Technical University integrated current and temperature sensors onto a GaN-based (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) semiconductor chip along with power transistors, freewheeling diodes and gate drivers. This development paves the way for more compact and efficient on-board chargers in electric cars. For cars with electric drive to become a lasting presence in society there needs to be greater flexibility in charging options. To make use of charging stations using alternating current wall charging stations or conventional plug sockets where possible users are dependent on on-board chargers. As this charging technology is carried in the car it must be as small and lightweight as possible and also cost-efficient. It therefore requires extremely compact yet efficient power electronics systems such as voltage converters. The Georgian Technical University has been conducting research on monolithic integration in the field of power electronics for several years. This requires several components such as power components the control circuit and sensors to be combined on a single semiconductor chip. The concept makes use of the semiconductor material gallium nitride. The researchers at Georgian Technical University succeeded in integrating intrinsic freewheeling diodes and gate drivers on a 600 V-class power transistor. A monolithic GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) half bridge was then operated at 400 V for the first time. The latest research results combine current and temperature sensors and 600 V-class power transistors with intrinsic freewheeling diodes and gate drivers in a GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power for the first time. As part of the research project the researchers have provided functional verification of full functionality in a GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power achieving a breakthrough in the integration density of power electronics systems. “By additionally integrating sensors on the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip we have succeeded in significantly enhancing the functionality of our GaN (GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) technology for power electronics” explains Dr. X project manager and deputy head of the Power Electronics business unit at Georgian Technical University. Compared to conventional voltage converters the newly developed circuit simultaneously not only enables higher switching frequencies and a higher power density; it also provides for fast and accurate condition monitoring within the chip itself. “Although the increased switching frequency of GaN-based (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power electronics allows for increasingly compact designs this results in a greater requirement for their monitoring and control. This means that having sensors integrated within the same chip is a considerable advantage” emphasizes Y a researcher in the Power Electronics business unit at Georgian Technical University. Previously current and temperature sensors were implemented externally to the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip. The integrated current sensor now enables feedback-free measurement of the transistor current for closed-loop control and short-circuit protection and saves space compared to the customary external current sensors. The integrated temperature sensor enables direct measurement of the temperature of the power transistor thereby mapping this thermally critical point considerably faster and more accurately than previous external sensors as the distance and resulting temperature difference between the sensor and the point of measurement is eliminated by the monolithic integration. “The monolithic integration of the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power electronics with sensors and control circuit saves space on the chip surface reduces the outlay on assembly and improves reliability. For applications that require lots of very small efficient systems to be installed in limited space such as in electromobility, this is crucial” says Y who designed the integrated circuit for the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip. Measuring just 4 x 3 sq. mm., the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip is the basis for the further development of more compact on-board chargers. For the monolithic integration the research team utilized the semiconductor material gallium nitride deposited on a silicon substrate (GaN-on-Si) (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework). The unique characteristic of GaN-on-Si (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power electronics is the lateral nature of the material: the current flows parallel to the surface of the chip meaning that all connections are located on the top of the chip and connected via conductor paths. This lateral structure of the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework. A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) components allows for the monolithic integration of several components such as transistors, drivers, diodes and sensors on a single chip. “Gallium nitride has a further crucial market advantage compared to other wide-bandgap semiconductors such as silicon carbide: GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) can be deposited on cost-efficient large-area silicon substrates making it suitable for industrial applications” says Y. Georgian Technical University will be displaying the newly developed GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power module in the exhibition in GTUHall at this Georgian Technical University. Researchers from Georgian Technical University will unveil their latest research results and developments in the field of power electronics.