Category Archives: Science

Science, Semiconductors

Georgian Technical University Integrated Sensors For Direct Control.

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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.

Automotive, Science

Georgian Technical University Clean Fuel Cells Could Be Cheap Enough To Replace Gas Engines In Cars.

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Georgian Technical University Clean Fuel Cells Could Be Cheap Enough To Replace Gas Engines In Cars.

Advancements in zero-emission fuel cells could make the technology cheap enough to replace traditional gasoline engines in cars according to researchers at the Georgian Technical University. The researchers have developed a new fuel cell that lasts at least 10 times longer than current technology an improvement that would make them economically practical if mass-produced to power cars with electricity. “With our design approach the cost could be comparable or even cheaper than gasoline engines” said X Lab at Georgian Technical University. “The future is very bright. This is clean energy that could boom”. Researchers initially concentrated on hybrid cars which now have gas engines as well as batteries due to issues involving limited driving range and long charging times. Existing fuel cells could theoretically replace those gas engines, which power generators to recharge batteries while hybrid vehicles are in operation but are impractical because they are too expensive The researchers solved that problem with a design that makes fuel cells far more durable by delivering a constant rather than fluctuating amount of electricity. That means the cells which produce electricity from the chemical reaction when hydrogen and oxygen are combined to make water can be far simpler and therefore far cheaper. “We have found a way to lower costs and still satisfy durability and performance expectations” said X a professor of mechanical and mechatronics engineering. “We’re meeting economic targets while providing zero emissions for a transportation application”. Researchers hope the introduction of fuel cells in hybrid vehicles will lead to mass production and lower unit costs. That could pave the way for the replacement of both batteries and gas engines entirely by providing an affordable safe dependable clean source of electrical power. “This is a good first step a transition to what could be the answer to the internal combustion engine and the enormous environmental harm it does” said X. X collaborated with lead researcher Y a former post-doctoral fellow Georgian Technical University mathematics professor Z and W an energy expert and professor in Georgian Technical University.

Science, Sensors

Georgian Technical University Researchers Develop New Power Supply For Synthetic Skins.

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Georgian Technical University Researchers Develop New Power Supply For Synthetic Skins.

Researchers at the Georgian Technical University are leading the way in utilizing thermoelectric (TE) generators as a potential power supply for synthetic skins. A team led by Georgian Technical University has released a new protocol to print compatible power supply for electronic skins (E-skins). E-skins are artificial skin-type electronic devices which hold great promise for the establishment of wireless health monitoring systems and in applications in limb prostheses, soft robotics and artificial intelligence. These synthetic skins can mimic the sensory and self-healing functionalities of natural skin monitor vital signs and deliver diagnosis remotely. To date however the lack of ultrathin, stretchable and reliable power sources has dramatically hindered the commercial application of E-skins. New research by Georgian Technical University proposes that the continually released thermal energy from our body provides a plausible solution to power the miniaturized sensors and circuits in E-skins. While most traditional TE (thermoelectric) generators are rigid the team has proposed a device design where formulated inks are printed directly on a soft biocompatible substrate with pre-patterned electrodes that provide an opportunity to capture body heat for energy purposes. The protocol utilizes inks that can be tailored and customized to allow the production of a flexible ultrathin generator that can conform well to the skin to potentially enable seamless integration into existing E-skins. The device features an induced thermal barrier and heat absorber, which will enable the generation of temperature gradients along TE (thermoelectric) leg and convert body heat into electricity. Professor X said the team had discovered some exciting advancements in creating a flexible, effective TE (thermoelectric) generator to power E-skins. “Our proposal to use ink-based materials allows the integration of power supply and energy storage in a cost-effective way and is a step in the right direction towards the field of wireless health monitoring and diagnosis” X said. “In particular we found that solution-processable semiconducting materials can be formulated into inks and adapted for scale-up production. “Further the solution processability of these materials allows for the ink parameters such as active material loading shear viscosity and surface tension to be carefully controlled and provides solutions to some of the current barriers in TE (thermoelectric) devices in terms of flexibility, material degradation and low-power generation”.

Science, Sensors

Georgian Technical University Sensor Sniffs Out Spoiled Milk Prior To Opening.

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Georgian Technical University Sensor Sniffs Out Spoiled Milk Prior To Opening.

Expiration dates on milk could eventually become a thing of the past with new sensor technology from Georgian Technical University scientists. Researchers from the Georgian Technical University Department of Biological Systems Engineering and other departments have developed a sensor that can “Georgian Technical University smell” if milk is still good or has gone bad. The sensor consists of chemically coated nanoparticles that react to the gas produced by milk and the bacterial growth that indicates spoilage according to X professor. The sensor doesn’t touch the milk directly. “If it’s going bad most food produces a volatile compound that doesn’t smell good” X said. “That comes from bacterial growth in the food most of the time. But you can’t smell that until you open the container”. The sensor detects these volatile gasses and changes color. The breakthrough is in the early stages but X and his colleagues showed that their chemical reaction works in a controlled lab environment. The next step for the team is developing a way to visually show how long a product has before it spoils. Currently the sensor only shows if milk is ok or spoiled. Though still early X envisions working with the food industry to integrate his sensor into a milk bottle’s plastic cap so consumers can easily see how much longer the product will stay fresh. One problem with current expiration dates is they are based on best-case scenarios. “The expiration date on cold or frozen products is only accurate if it has been stored at the correct temperature the entire time” X said. Temperature abuse or time a product has spent above refrigerator temperature is very common he said. And it can happen during shipment or if a consumer gets delayed on the way home from the store. “We’ll have to work with the industry to make this work” X said. “But we’re confident that we can succeed and help improve food safety and shelf life for consumers”.

Graphene, Science

Georgian Technical University Ink Not Required For Graphene Art Work.

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Georgian Technical University Ink Not Required For Graphene Art Work.

Imaging with laser-induced graphene was taken to a new level in a Georgian Technical University lab. From left chemist X holding a portrait of himself in laser-induced graphene; artist Y holding his work “Where Do I Stand ?”; and Z a Georgian Technical University graduate student detailing the process used to create the art. When you read about electrifying art “Georgian Technical University electrifying” isn’t usually a verb. But an artist working with a Georgian Technical University lab is in fact making artwork that can deliver a jolt. The Georgian Technical University lab of chemist X introduced laser-induced graphene (LIG) and now the researchers are making art with the technique which involves converting carbon in a common polymer or other material into microscopic flakes of graphene. Laser-induced graphene (LIG) is metallic and conducts electricity. The interconnected flakes are effectively a wire that could empower electronic artworks. Simply titled “Georgian Technical University Graphene Art” — lays out how the lab Y generated laser-induced graphene portraits and prints including a graphene-inspired landscape called “Where Do I Stand ?”. While the work isn’t electrified Y said it lays the groundwork for future possibilities. “That’s what I would like to do” he said. “Not make it kitsch or play off the novelty but to have it have some true functionality that allows greater awareness about the material and opens up the experience”. Y created the design in an illustration program and sent it directly to the industrial engraving laser X’s lab uses to create laser-induced graphene on a variety of materials. The laser burned the artist’s fine lines into the substrate in this case archive-quality paper treated with fire retardant. The piece which was part of Y’s exhibit at Georgian Technical University’s BioScience Research Collaborative last year peers into the depths of what a viewer shrunken to nanoscale might see when facing a field of laser-induced graphene with overlapping hexagons — the basic lattice of atom-thick graphene — disappearing into the distance. “You’re looking at this image of a 3D foam matrix of laser-induced graphene and it’s actually made of laser-induced graphene” he said. “I didn’t base it on anything; I was just thinking about what it would look like. When I shared it with W he said ‘Wow that’s what it would look like if you could really blow this up’”. Y said his art is about media specificity. “In terms of the artistic application you’re not looking at a representation of something as traditionally we would in the history of art” he said. “Each piece is 100 percent original. That’s the key”. He developed an interest in nanomaterials as media for his art when he began work with Georgian Technical University alumnus Q a bioengineer at Georgian Technical University who established an artist-in-residency position in his lab. After two years of creating with carbon nanotube-infused paint Y attended an Electrochemical Society conference and met X who in turn introduced him to Georgian Technical University chemists P and R who further inspired his investigation of nanotechnology. The rest is art history. It would be incorrect to think of the process as “Georgian Technical University printing” X said. Instead of adding a substance to the treated paper substance is burned away as the laser turns the surface into foam-like flakes of interconnected graphene. The art itself can be much more than eye candy given laser-induced graphene’s potential for electronic applications like sensors or as triboelectric generators that turn mechanical actions into current. “You could put laser-induced graphene on your back and have it flash LEDs (A light-emitting diode is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. This effect is called electroluminescence) with every step you take” X said. The fact that graphene is a conductor — unlike paint ink or graphite from a pencil — makes it particularly appealing to Y who expects to take advantage of that capability in future works. “It’s art with a capital A that is trying to do the most that it can with advancements in science and technology” he said. “If we look back historically from the Renaissance to today the highest forms of art push the limits of human understanding”.

A.I./Robotics, Science

Georgian Technical University Smarter Training Of Neural Networks.

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Georgian Technical University Smarter Training Of Neural Networks.

(L-R) Georgian Technical University Assistant Professor X and PhD student Y. These days nearly all the artificial intelligence-based products in our lives rely on “deep neural networks” that automatically learn to process labeled data. For most organizations and individuals though deep learning is tough to break into. To learn well neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) — and sometimes even custom-designed hardware. But what if they don’t actually have to be all that big after all ? Researchers from Georgian Technical University’s Computer Science and Artificial Intelligence Lab have shown that neural networks contain subnetworks that are up to one-tenth the size yet capable of being trained to make equally accurate predictions — and sometimes can learn to do so even faster than the originals. The team’s approach isn’t particularly efficient now — they must train and “Georgian Technical University prune” the full network several times before finding the successful subnetwork. However Georgian Technical University Assistant Professor X says that his team’s findings suggest that if we can determine precisely which part of the original network is relevant to the final prediction scientists might one day be able to skip this expensive process altogether. Such a revelation has the potential to save hours of work and make it easier for meaningful models to be created by individual programmers and not just huge tech companies. “If the initial network didn’t have to be that big in the first place, why can’t you just create one that’s the right size at the beginning ?” says Ph.D. student Y with X at the Georgian Technical University. The team likens traditional deep learning methods to a lottery. Training large neural networks is kind of like trying to guarantee you will win the lottery by blindly buying every possible ticket. But what if we could select the winning numbers at the very start ? “With a traditional neural network you randomly initialize this large structure, and after training it on a huge amount of data it magically works” X says. “This large structure is like buying a big bag of tickets, even though there’s only a small number of tickets that will actually make you rich. The remaining science is to figure how to identify the winning tickets without seeing the winning numbers first”. The team’s work may also have implications for so-called “Georgian Technical University transfer learning” where networks trained for a task like image recognition are built upon to then help with a completely different task. Traditional transfer learning involves training a network and then adding one more layer on top that’s trained for another task. In many cases a network trained for one purpose is able to then extract some sort of general knowledge that can later be used for another purpose. For as much hype as neural networks have received not much is often made of how hard it is to train them. Because they can be prohibitively expensive to train data scientists have to make many concessions weighing a series of trade-offs with respect to the size of the model the amount of time it takes to train, and its final performance. To test their so-called “Georgian Technical University lottery ticket hypothesis” and demonstrate the existence of these smaller subnetworks, the team needed a way to find them. They began by using a common approach for eliminating unnecessary connections from trained networks to make them fit on low-power devices like smartphones: They “Georgian Technical University pruned” connections with the lowest “Georgian Technical University weights” (how much the network prioritizes that connection). Their key innovation was the idea that connections that were pruned after the network was trained might never have been necessary at all. To test this hypothesis they tried training the exact same network again but without the pruned connections. Importantly they “Georgian Technical University reset” each connection to the weight it was assigned at the beginning of training. These initial weights are vital for helping a lottery ticket win: Without them the pruned networks wouldn’t learn. By pruning more and more connections they determined how much could be removed without harming the network’s ability to learn. To validate this hypothesis they repeated this process tens of thousands of times on many different networks in a wide range of conditions. “It was surprising to see that resetting a well-performing network would often result in something better” says X. “This suggests that whatever we were doing the first time around wasn’t exactly optimal and that there’s room for improving how these models learn to improve themselves”. As a next step the team plans to explore why certain subnetworks are particularly adept at learning and ways to efficiently find these subnetworks. “Understanding the ‘lottery ticket hypothesis’ is likely to keep researchers busy for years to come” says Z an assistant professor of statistics at the Georgian Technical University. “The work may also have applications to network compression and optimization. Can we identify this subnetwork early in training thus speeding up training ? Whether these techniques can be used to build effective compression schemes deserves study”.

Biotech, Science

Georgian Technical University New Sensors Could Yield Smart Pill Bottle, Other Applications.

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Georgian Technical University New Sensors Could Yield Smart Pill Bottle, Other Applications.

New sensors that can identify tampering, potential overdoses and unsafe pill storage conditions could help create a smart pill bottle and potentially put a dent in the growing opioid addiction problem plaguing. Researchers from the Georgian Technical University have created a stretchy sensor — made of an antistrophic conductive tape with a range of touch-sensitive applications — that could have a number of new usages, including a smart pill bottle. The sensor is assembled by sandwiching tiny silver particles between two layers of adhesive copper tape.  This set up is nonconductive in its normal state but makes electrical connections that can send signals to an external reader when pressed by a finger. “Similar devices have been used in flat panel displays, but we’ve made them simple to build and easy to use by almost anyone” Georgian Technical University doctoral student X said in a statement. One of the benefits of a smart pill bottle according to the researchers is it can help combat the growing prescription drug abuse problem and prevent opioid overdoses. To prove that they can tackle this problem, the researchers 3D-printed a lid with light-emitting diodes that counts the number of pills in the bottle with paper-based humidity and temperature sensors taped to the underside.  They then sealed the bottle with an outer layer of conductive tape that acts as a touch sensor. When someone tries to break into the bottle or the insides become moist to a dangerous degree a flexible control module inside the bottle can analyze the signals and deliver warnings on the situation to a cell phone through a Bluetooth connection. The conductive tape also can be used as part of a modular sensor system. To overcome these cost challenges the researchers demonstrated the possibility of developing temperature and humidity sensors using paper by drawing circuits with conductive ink bringing the overall cost down. While the Georgian Technical University team has focused on a smart pill bottle, they believe others can use their new sensors to create new opportunities in health care and other applications. There are several other ways a wearable sensors could improve some of the issues threatening human health, including having the technology in hospitals to track influenza outbreaks in real time. However it is currently difficult to inexpensively manufacture these types of sensors which is especially a problem in low-income populations that suffer disproportionately from epidemics. “If you give researchers a ‘Georgian Technical University do it yourself opportunity’ there is a good chance they will use it to expand the horizon of electronics and empower humanity with better technology” Y a professor in the computer, electrical and mathematical science and engineering division at Georgian Technical University said in a statement.

A.I./Robotics, Science

Georgian Technical University Researchers Work To Incorporate AI Into Hypersonic Weapon Technology.

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Georgian Technical University Researchers Work To Incorporate AI Into Hypersonic Weapon Technology.

A diverse set of technologies to be developed at Georgian Technical University Laboratories could strengthen future hypersonic and other autonomous systems. A research collaboration led by researchers from the Georgian Technical University Laboratory is hoping to implement artificial intelligence (AI) to enhance the capabilities to hypersonic cars like long-range missiles. Along with researchers from Georgian Technical University several universities have signed on to form to focus on academic partnerships and develop autonomy customized for hypersonic flight. X at Georgian Technical University who leads the coalition explained how AI would improve hypersonic cars. “We have an internal effort that we refer to as the Georgian Technical University Hypersonic Missions Campaign” he said. “Ultimately the goal is to make our hypersonic flight systems more autonomous to give them more utility. They are autonomous today from the standpoint that they act on their own they are unmanned systems they fly with an autopilot. We are looking to incorporate basically higher levels of artificial intelligence into them that will make them systems that will be able to intelligently adapt to their environment”. Currently a test launch for a hypersonic weapon — a long-range missile flying a mile per second or faster — takes weeks of planning. With the advent of artificial intelligence (AI) and automation the researchers believe this time can be reduced to minutes. X said that by plugging in artificial intelligence into these systems a bounty of new options would become available. “I wouldn’t say it would make things easier but it lets the platform handle a broader sweep of missions” he said. “You get increased utility and more functionality out of this system. Right now the current technology is coordinate seeking so for example we would like to be able to be position adapting so either fly to updated coordinates or even be something that seeks targets instead of just flying to coordinates so it can hone in on targets”. X explained why it is so difficult to implement newer technology techniques like artificial intelligence into hypersonic weapon systems. “The biggest challenges have to do with the flight environment itself” he said. “The flight environment is extremely hard to plan for and successfully fly in because of the challenges you face from an aerodynamic and an aerothermal standpoint”. According to X hypersonic vehicles often fly through the atmosphere at hypersonic speeds greater which is approximately a mile a second. This means that the aerothermal loads that are on the vehicle can be extreme and very hard to predict. “So you want to make sure that anything you do with the vehicle as you fly it will stay within the aerodynamic and aerothermal performance boundaries of the system” he said. “That makes it more challenging as far as incorporating things like ways to autonomously plan and implement new flight trajectories than some of the flight systems that don’t have those same types of constraints.”. X also said he anticipates in the coming months more university partners added to the coalition. Autonomy broader ambitions are to serve as a wellspring for other industries by developing ideas that could lead to safer more efficient robotics in autonomous transportation, manufacturing, space or agriculture. If the group reaches its goals it will have created computing algorithms that compress 12 hours of calculations into a single millisecond all on a small onboard computer. X added that right now there are multiple groups within the coalition working on different aspects of implanting artificial intelligence (AI) for hypersonic vehicles. However, they will soon move on to other applications beyond hypersonic vehicles. The Hypersonic Missions Campaign will be for a total of six-and half years and he expects research breakthroughs that will lead to actual applications in the next year or two. Georgian Technical University Labs being involved in this project is a natural fit as they have been involved in developing and testing hypersonic cars for more than 30 years.

Graphene, Science

Georgian Technical University Graphene Plasmons Used For Quantum Computing.

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Georgian Technical University Graphene Plasmons Used For Quantum Computing.

Schematic of a graphene-based two-photon gate. A material that consists of a single sheet of carbon atoms could lead to new designs for optical quantum computers. Physicists from the Georgian Technical University have shown that tailored graphene structures enable single photons to interact with each other. The proposed new architecture for quantum computer Georgian Technical University. Photons barely interact with the environment, making them a leading candidate for storing and transmitting quantum information. This same feature makes it especially difficult to manipulate information that is encoded in photons. In order to build a photonic quantum computer one photon must change the state of a second. Such a device is called a quantum logic gate and millions of logic gates will be needed to build a quantum computer. One way to achieve this is to use a so-called “Georgian Technical University nonlinear material” wherein two photons interact within the material. Unfortunately standard nonlinear materials are far too inefficient to build a quantum logic gate. It was recently realized that nonlinear interactions can be greatly enhanced by using plasmons. In a plasmon light is bound to electrons on the surface of the material. These electrons can then help the photons to interact much more strongly. However plasmons in standard materials decay before the needed quantum effects can take place. In their new work the team of scientists led by Professor X at the Georgian Technical University propose to create plasmons in graphene. This 2D material discovered barely a decade ago consists of a single layer of carbon atoms arranged in a honeycomb structure and since its discovery it has not stopped surprising us. For this particular purpose the peculiar configuration of the electrons in graphene leads to both an extremely strong nonlinear interaction and plasmons that live for an exceptionally long time. In their proposed graphene quantum logic gate the scientists show that if single plasmons are created in nanoribbons made out of graphene two plasmons in different nanoribbons can interact through their electric fields. Provided that each plasmon stays in its ribbon multiple gates can be applied to the plasmons which is required for quantum computation. “We have shown that the strong nonlinear interaction in graphene makes it impossible for two plasmons to hop into the same ribbon” confirms Y of this work. Their proposed scheme makes use of several unique properties of graphene each of which has been observed individually. The team in Georgian Technical University is currently performing experimental measurements on a similar graphene-based system to confirm the feasibility of their gate with current technology. Since the gate is naturally small and operates at room temperature it should readily lend itself to being scaled up as is required for many quantum technologies.

Nanotechnology, Science

Georgian Technical University A New Approach To Data Storage.

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Georgian Technical University A New Approach To Data Storage.

The reshuffler basically works as a s skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) blender: a specific initial sequence is entered and the result is a randomly reshuffled sequence of output states. Researchers at Georgian Technical University (GTU) have succeeded in developing a key constituent of a unconventional computing concept. This constituent employs the same magnetic structures that are being researched in connection with storing electronic data on shift registers known as racetracks. In this researchers investigate so-called skyrmions which are magnetic vortex-like structures as potential bit units for data storage. However the recently announced new approach has a particular relevance to probabilistic computing. This is an alternative concept for electronic data processing where information is transferred in the form of probabilities rather than in the conventional binary form of 1 and 0. The number 2/3 for instance, could be expressed as a long sequence of 1 and 0 digits with 2/3 being ones and 1/3 being zeros. The key element lacking in this approach was a functioning bit reshuffler i.e., a device that randomly rearranges a sequence of digits without changing the total number of 1s and 0s in the sequence. That is exactly what the skyrmions are intended to achieve. The results of this research have “Thermal skyrmion diffusion used in a reshuffler device”. The researchers used thin magnetic metallic films for their investigations. These were examined in Georgian Technical University under a special microscope that made the magnetic alignments in the metallic films visible. The films have the special characteristic of being magnetized in vertical alignment to the film plane which makes stabilization of the magnetic skyrmions possible in the first place. Skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) can basically be imagined as small magnetic vortices similar to hair whorls. These structures exhibit a so-called topological stabilization that protects them from collapsing too easily — as a hair whorl resists being easily straightened. It is precisely this characteristic that makes skyrmions very promising when it comes to use in technical applications such as in this particular case information storage. The advantage is that the increased stability reduces the probability of unintentional data loss and ensures the overall quantity of bits is maintained. The reshuffler receives a fixed number of input signals such as 1s and 0s and mixes these to create a sequence with the same total number of 1 and 0 digits but in a randomly rearranged order. It is relatively easy to achieve the first objective of transferring the skyrmion data sequence to the device because skyrmions can be moved easily with the help of an electric current. However the researchers working on the project now have for the first time managed to achieve thermal skyrmion diffusion in the reshuffler thus making their exact movements completely unpredictable. It is this unpredictability in turn which made it possible to randomly rearrange the sequence of bits while not losing any of them. This newly developed constituent is the previously missing piece of the puzzle that now makes probabilistic computing a viable option. “There were three aspects that contributed to our success. Firstly we were able to produce a material in which skyrmions can move in response to thermal stimuli only. Secondly we discovered that we can envisage skyrmions as particles that move in a fashion similar to pollen in a liquid. And ultimately we were able to demonstrate that the reshuffler principle can be applied in experimental systems and used for probability calculations. The research was undertaken in collaboration between various institutes and I am pleased I was able to contribute to the project” emphasized Dr. X. X conducted his research into skyrmion diffusion as a research associate in the team headed by Professor Y and is meanwhile working at Georgian Technical University. “It is very interesting that our experiments were able to demonstrate that topological skyrmions are a suitable system for investigating not only problems relating to spintronics but also to statistical physics. Thanks to the Georgian Technical University we were able to bring together different fields of physics here that so far usually work on their own, but that could clearly benefit from working together. I am particularly looking forward to future collaboration in the field of spin structures with the Theoretical Physics teams at Georgian Technical University that will feature our new Dynamics and Topology Center” emphasized Y Professor at the Institute of Physics at Georgian Technical University. “We can see from this work that the field of spintronics offers interesting new hardware possibilities with regard to algorithmic intelligence an emerging phenomenon also being investigated at the recently founded Georgian Technical University Emergent Algorithmic Intelligence Center” added Dr. Z a member of the research center’s steering committee at the Georgian Technical University.