Computers Math

Artificial intelligence (AI) is all the rage lately in the public eye. How AI is being incorporated to the advantage of our everyday life despite its rapid development, however, remains an elusive topic that deserves the attention of many scientists. While in theory AI can replace, or even displace, human beings from their positions, the challenge remains on how different industries and institutions can take advantage of this technological advancement and not drown in it.
Recently, a team of researchers at the Hong Kong University of Science and Technology (HKUST) conducted an ambitious study of AI applications on the education front, examining how AI could enhance grading while observing human participants’ behavior in the presence of a computerized companion. They found that teachers were generally receptive to AI’s input — until both sides came to an argument on who should reign supreme. This very much resembles how human beings interact with one another when a new member forays into existing territory.
The research was conducted by HKUST Department of Computer Science and Engineering Ph.D. candidate Chengbo Zheng and four of his teammates under the supervision of Associate Professor Prof. Xiaojuan MA. They developed an AI group member named AESER (Automated Essay ScorER) and separated twenty English teachers into ten groups to investigate the impact of AESER in a group discussion setting, where the AI would contribute in opinion deliberation, asking and answering questions and even voting for the final decision. In this study, designed akin to the controlled “Wizard of Oz” research method, a deep learning model and a human researcher would form joint input to AESER, which would then exchange views and conduct discussions with other participants in an online meeting room.
While the team expected AESER to promote objectivity and provide novel perspectives that would otherwise be overlooked, potential challenges were soon revealed. First, there was the risk of conformity, where the engagement of AI would soon create a majority to thwart discussions. Second, views provided by AESER were found to be rigid and even stubborn, which frustrated the participants when they found that an argument could never be “won.” Many also did not think AI’s input should be given equal weight and are more fit to play the role of an assistant to actual human work.
“At this stage, AI is deemed somewhat ‘stubborn’ by human collaborators, for good and bad,” noted Prof Ma. “On the one hand, AI is stubborn so it does not fear to express its opinions frankly and openly. However, human collaborators feel disengaged when they could not meaningfully persuade AI to change its view. Humans varying attitudes towards AI. Some consider it to be a single intelligent entity while others regard AI as the voice of collective intelligence that emerges from big data. Discussions about issues such as authority and bias thus arise.”
The immediate next step for the team involves expanding its scope to gathermore quantitative data, which will provide more measurable and precise insights into how AI impacts group decision-making. They are also looking to explore large language models (LLMs) such as ChatGPT into the study, which could potentially bring new insights and perspectives to group discussions. More

A University of Minnesota Twin Cities team has, for the first time, synthesized a thin film of a unique topological semimetal material that has the potential to generate more computing power and memory storage while using significantly less energy. The researchers were also able to closely study the material, leading to some important findings about the physics behind its unique properties.
The study is published in Nature Communications, a peer-reviewed scientific journal that covers the natural sciences and engineering.
As evidenced by the United States’ recent CHIPS and Science Act, there is a growing need to increase semiconductor manufacturing and support research that goes into developing the materials that power electronic devices everywhere. While traditional semiconductors are the technology behind most of today’s computer chips, scientists and engineers are always looking for new materials that can generate more power with less energy to make electronics better, smaller, and more efficient.
One such candidate for these new and improved computer chips is a class of quantum materials called topological semimetals. The electrons in these materials behave in different ways, giving the materials unique properties that typical insulators and metals used in electronic devices do not have. For this reason, they are being explored for use in spintronic devices, an alternative to traditional semiconductor devices that leverage the spin of electrons rather than the electrical charge to store data and process information.
In this new study, an interdisciplinary team of University of Minnesota researchers were able to successfully synthesize such a material as a thin film — and prove that it has the potential for high performance with low energy consumption.
“This research shows for the first time that you can transition from a weak topological insulator to a topological semimetal using a magnetic doping strategy,” said Jian-Ping Wang, a senior author of the paper and a Distinguished McKnight University Professor and Robert F. Hartmann Chair in the University of Minnesota Department of Electrical and Computer Engineering. “We’re looking for ways to extend the lifetimes for our electrical devices and at the same time lower the energy consumption, and we’re trying to do that in non-traditional, out-of-the-box ways.”
Researchers have been working on topological materials for years, but the University of Minnesota team is the first to use a patented, industry-compatible sputtering process to create this semimetal in a thin film format. Because their process is industry compatible, Wang said, the technology can be more easily adopted and used for manufacturing real-world devices.

“Every day in our lives, we use electronic devices, from our cell phones to dishwashers to microwaves. They all use chips. Everything consumes energy,” said Andre Mkhoyan, a senior author of the paper and Ray D. and Mary T. Johnson Chair and Professor in the University of Minnesota Department of Chemical Engineering and Materials Science. “The question is, how do we minimize that energy consumption? This research is a step in that direction. We are coming up with a new class of materials with similar or often better performance, but using much less energy.”
Because the researchers fabricated such a high-quality material, they were also able to closely analyze its properties and what makes it so unique.
“One of the main contributions of this work from a physics point of view is that we were able to study some of this material’s most fundamental properties,” said Tony Low, a senior author of the paper and the Paul Palmberg Associate Professor in the University of Minnesota Department of Electrical and Computer Engineering. “Normally, when you apply a magnetic field, the longitudinal resistance of a material will increase, but in this particular topological material, we have predicted that it would decrease. We were able to corroborate our theory to the measured transport data and confirm that there is indeed a negative resistance.”
Low, Mkhoyan, and Wang have been working together for more than a decade on topological materials for next generation electronic devices and systems — this research wouldn’t have been possible without combining their respective expertise in theory and computation, material growth and characterization, and device fabrication.
“It not only takes an inspiring vision but also great patience across the four disciplines and a dedicated group of team members to work on such an important but challenging topic, which will potentially enable the transition of the technology from lab to industry,” Wang said.
In addition to Low, Mkhoyan, and Wang, the research team included University of Minnesota Department of Electrical and Computer Engineering researchers Delin Zhang, Wei Jiang, Onri Benally, Zach Cresswell, Yihong Fan, Yang Lv, and Przemyslaw Swatek; Department of Chemical Engineering and Materials Science researcher Hwanhui Yun; Department of Physics and Astronomy researcher Thomas Peterson; and University of Minnesota Characterization Facility researchers Guichuan Yu and Javier Barriocanal.
This research is supported by SMART, one of seven centers of nCORE, a Semiconductor Research Corporation program, sponsored by National Institute of Standards and Technology (NIST). T.P. and D.Z. were partly supported by ASCENT, one of six centers of JUMP, a Semiconductor Research Corporation program that is sponsored by MARCO and DARPA. This work was partially supported by the University of Minnesota’s Materials Research Science and Engineering Center (MRSEC) program under award number DMR-2011401 (Seed). Parts of this work were carried out in the Characterization Facility of the University of Minnesota Twin Cities, which receives partial support from the National Science Foundation through the MRSEC (Award NumberDMR-2011401). Portions of this work were conducted in the Minnesota Nano Center, which is supported by the NSF Nano Coordinated Infrastructure Network (NNCI) under Award Number ECCS-2025124. More

The future of electronics will be based on novel kinds of materials. Sometimes, however, the naturally occurring topology of atoms makes it difficult for new physical effects to be created. To tackle this problem, researchers at the University of Zurich have now successfully designed superconductors one atom at a time, creating new states of matter.
What will the computer of the future look like? How will it work? The search for answers to these questions is a major driver of basic physical research. There are several possible scenarios, ranging from the further development of classical electronics to neuromorphic computing and quantum computers. The common element in all these approaches is that they are based on novel physical effects, some of which have so far only been predicted in theory. Researchers go to great lengths and use state-of-the-art equipment in their quest for new quantum materials that will enable them to create such effects. But what if there are no suitable materials that occur naturally?
Novel approach to superconductivity
In a recent study published in Nature Physics, the research group of UZH Professor Titus Neupert, working closely together with physicists at the Max Planck Institute of Microstructure Physics in Halle (Germany), presented a possible solution. The researchers made the required materials themselves — one atom at a time. They are focusing on novel types of superconductors, which are particularly interesting because they offer zero electrical resistance at low temperatures. Sometimes referred to as “ideal diamagnets,” superconductors are used in many quantum computers due to their extraordinary interactions with magnetic fields. Theoretical physicists have spent years researching and predicting various superconducting states. “However, only a small number have so far been conclusively demonstrated in materials,” says Professor Neupert.
Two new types of superconductivity
In their exciting collaboration, the UZH researchers predicted in theory how the atoms should be arranged to create a new superconductive phase, and the team in Germany then conducted experiments to implement the relevant topology. Using a scanning tunneling microscope, they moved and deposited the atoms in the right place with atomic precision. The same method was also used to measure the system’s magnetic and superconductive properties. By depositing chromium atoms on the surface of superconducting niobium, the researchers were able to create two new types of superconductivity. Similar methods had previously been used to manipulate metal atoms and molecules, but until now it has never been possible to make two-dimensional superconductors with this approach.
The results not only confirm the physicists’ theoretical predictions, but also give them reason to speculate about what other new states of matter might be created in this way, and how they could be used in the quantum computers of the future. More

Researchers have demonstrated a continuous roller printing process that can pick up and transfer over 75,000 micrometer-scale semiconductor devices in a single roll with very high accuracy. The new method paves the way to creating large-scale arrays of optical components and could be used to rapidly manufacture micro-LED displays.
Micro-LED display technology is of great interest because it can accomplish highly accurate color rendering with high speed and resolution while using little power. These displays can be applied in a wide range of formats including smartphone screens, virtual and augmented reality devices and large displays several meters across. For larger micro-LED displays, in particular, the challenges of integrating millions of tiny LEDs — which are sometimes smaller than a grain of fine sand — onto an electronic control backplane are enormous.
“Transferring micrometer-scale semiconductor devices from their native substrate to a variety of receiving platforms is a challenge being tackled internationally by both academic research groups and industries,” said research team leader Eleni Margariti from the University of Strathclyde in the UK. “Our roller-based printing process offers a way to achieve this in a scalable manner while meeting the demanding accuracy necessary for this application.”
In the journal Optical Materials Express, the researchers report that their new roller technology can match the designed device layout with an accuracy of less than 1 micron. The setup is also inexpensive and simple enough to be constructed in locations with limited resources.
“This printing process could also be used for other types of devices including silicon and printed electronics such as transistors, sensors and antennas for flexible and wearable electronics, smart packaging and radio-frequency identification tags,” said Margariti, who developed the new printing process. “It could also be useful for making photovoltaics and for biomedical applications such as drug delivery systems, biosensors and tissue engineering.”
Large-scale device transfer
Today’s semiconductor devices are typically manufactured on wafers using growth techniques that deposit exquisitely detailed, multi-layer semiconductor thin films onto semiconductor substrates. Compatibility issues between these thin film structures and the types of substrates suitable for this deposition constrain the ways in which the devices can be used.

“We wanted to improve the transfer of large numbers of semiconductor devices from one substrate to another to improve the performance and scaling of electronic systems used in applications such as displays and on-chip photonics, where the aim is to combine various materials that manipulate light on a very small scale,” said Margariti. “To be used for large-scale manufacturing, it is crucial to use methods that can transfer these devices efficiently, accurately and with minimal errors.”
The new approach starts with an array of tiny devices that are loosely attached to their growth substrate. The surface of a cylinder containing a slightly sticky silicone polymer film is then rolled over the suspended array of devices, allowing adhesive forces between the silicone and semiconductor to detach the devices from their growth substrate and array them on the cylinder drum. Because the printing process is continuous it can be used to simultaneously print numerous devices, which makes it highly efficient for large-scale production.
Highly accurate printing
“By carefully selecting the properties of the silicone and receiving substrate surface and the speed and mechanics of the rolling process, the devices can be successfully rolled over and released onto the receiver substrate while preserving the spatially arrayed format they had on the original substrate,” explained Margariti. “We also developed a custom analysis method that scans the printed sample for defects and provides the printing yield and positioning accuracy in just minutes.”
The researchers tested the new approach with gallium nitride on silicon (GaN/Si) semiconductor structures. GaN is the dominant semiconductor technology used for micro-LED displays, and using silicon substrates facilitated the preparation of the devices as suspended structures that could be picked up by the roller. They were able to transfer more than 99% of the devices in an array of over 76,000 individual elements with a spatial precision below a micron with no significant rotational errors.
Next, the researchers are working to further improve the accuracy of the printing process while also scaling up the number of devices that can be transferred at once. They also plan to test the method’s ability to combine different types of devices onto the same receiving platform and determine if it can be used to print to specific locations of the receiving platform. More

Many scholars, analysts, and other observers have suggested that resistance to innovation is an Achilles’ heel of authoritarian regimes. Such governments can fail to keep up with technological changes that help their opponents; they may also, by stifling rights, inhibit innovative economic activity and weaken the long-term condition of the country.
But a new study co-led by an MIT professor suggests something quite different. In China, the research finds, the government has increasingly deployed AI-driven facial-recognition technology to suppress dissent; has been successful at limiting protest; and in the process, has spurred the development of better AI-based facial-recognition tools and other forms of software.
“What we found is that in regions of China where there is more unrest, that leads to greater government procurement of facial-recognition AI, subsequently, by local government units such as municipal police departments,” says MIT economist Martin Beraja, who is co-author of a new paper detailing the findings.
What follows, as the paper notes, is that “AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation.”
The scholars call this state of affairs an “AI-tocracy,” describing the connected cycle in which increased deployment of the AI-driven technology quells dissent while also boosting the country’s innovation capacity.
The open-access paper, also called “AI-tocracy,” appears in the August issue of the Quarterly Journal of Economics. An abstract of the uncorrected proof was first posted online in March. The co-authors are Beraja, who is the Pentti Kouri Career Development Associate Professor of Economics at MIT; Andrew Kao, a doctoral candidate in economics at Harvard University; David Yang, a professor of economics at Harvard; and Noam Yuchtman, a professor of management at the London School of Economics.

To conduct the study, the scholars drew on multiple kinds of evidence spanning much of the last decade. To catalogue instances of political unrest in China, they used data from the Global Database of Events, Language, and Tone (GDELT) Project, which records news feeds globally. The team turned up 9,267 incidents of unrest between 2014 and 2020.
The researchers then examined records of almost 3 million procurementcontracts issued by the Chinese government between 2013 and 2019, from a database maintained by China’s Ministry of Finance. They found that local governments’ procurement of facial-recognition AI services and complementary public security tools — high-resolution video cameras — jumped significantly in the quarter following an episode of public unrest in that area.
Given that Chinese government officials were clearly responding to public dissent activities by ramping up on facial-recognition technology, the researchers then examined a follow-up question: Did this approach work to suppress dissent?
The scholars believe that it did, although as they note in the paper, they “cannot directly estimate the effect” of the technology on political unrest. But as one way of getting at that question, they studied the relationship between weather and political unrest in different areas of China. Certain weather conditions are conducive to political unrest. But in prefectures in China that had already invested heavily in facial-recognition technology, such weather conditions are less conducive to unrest compared to prefectures that had not made the same investments.
In so doing, the researchers also accounted for issues such as whether or not greater relative wealth levels in some areas might have produced larger investments in AI-driven technologies regardless of protest patterns. However, the scholars still reached the same conclusion: Facial-recognition technology was being deployed in response to past protests, and then reducing further protest levels.

“It suggests that the technology is effective in chilling unrest,” Beraja says.
Finally, the research team studied the effects of increased AI demand on China’s technology sector and found the government’s greater use of facial-recognition tools appears to be driving the country’s tech sector forward. For instance, firms that are granted procurement contracts for facial-recognition technologies subsequently produce about 49 percent more software products in the two years after gaining the government contract than they had beforehand.
“We examine if this leads to greater innovation by facial-recognition AI firms, and indeed it does,” Beraja says.
Such data — from China’s Ministry of Industry and Information Technology — also indicates that AI-driven tools are not necessarily “crowding out” other kinds of high-tech innovation.
Adding it all up, the case of China indicates how autocratic governments can potentially reach a near-equilibrium state in which their political power is enhanced, rather than upended, when they harness technological advances.
“In this age of AI, when the technologies not only generate growth but are also technologies of repression, they can be very useful” to authoritarian regimes, Beraja says.
The finding also bears on larger questions about forms of government and economic growth. A significant body of scholarly research shows that rights-granting democratic institutions do generate greater economic growth over time, in part by creating better conditions for technological innovation. Beraja notes that the current study does not contradict those earlier findings, but in examining the effects of AI in use, it does identify one avenue through which authoritarian governments can generate more growth than they otherwise would have.
“This may lead to cases where more autocratic institutions develop side by side with growth,” Beraja adds.
Other experts in the societal applications of AI say the paper makes a valuable contribution to the field.
“This is an excellent and important paper that improves our understanding of the interaction between technology, economic success, and political power,” says Avi Goldfarb, the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management at the University of Toronto. “The paper documents a positive feedback loop between the use of AI facial-recognition technology to monitor suppress local unrest in China and the development and training of AI models. This paper is pioneering research in AI and political economy. As AI diffuses, I expect this research area to grow in importance.”
For their part, the scholars are continuing to work on related aspects of this issue. One forthcoming paper of theirs examines the extent to which China is exporting advanced facial-recognition technologies around the world — highlighting a mechanism through which government repression could grow globally.
Support for the research was provided in part by the U.S. National Science Foundation Graduate Research Fellowship Program; the Harvard Data Science Initiative; and the British Academy’s Global Professorships program. More

The Big Data revolution has strained the capabilities of state-of-the-art electronic hardware, challenging engineers to rethink almost every aspect of the microchip. With ever more enormous data sets to store, search and analyze at increasing levels of complexity, these devices must become smaller, faster and more energy efficient to keep up with the pace of data innovation.
Ferroelectric field effect transistors (FE-FETs) are among the most intriguing answers to this challenge. Like traditional silicon-based transistors, FE-FETs are switches, turning on and off at incredible speed to communicate the 1s and 0s computers use to perform their operations.
But FE-FETs have an additional function that conventional transistors do not: their ferroelectric properties allow them to hold on to electrical charge.
This property allows them to serve as non-volatile memory devices as well as computing devices. Able to both store and process data, FE-FETs are the subject of a wide range of research and development projects. A successful FE-FET design would dramatically undercut the size and energy usage thresholds of traditional devices, as well as increase speed.
Researchers at the University of Pennsylvania School of Engineering and Applied Science have introduced a new FE-FET design that demonstrates record-breaking performances in both computing and memory.
A recent study published in Nature Nanotechnology led by Deep Jariwala, Associate Professor in the Department of Electrical and Systems Engineering (ESE), and Kwan-Ho Kim, a Ph.D. candidate in his lab, debuted the design. They collaborated with fellow Penn Engineering faculty members Troy Olsson, also Associate Professor in ESE, and Eric Stach, Robert D. Bent Professor of Engineering in the Department of Materials Science and Engineering (MSE) and Director of the Laboratory for Research on the Structure of Matter (LRSM).

The transistor layers a two-dimensional semiconductor called molybdenum disulfide (MoS2) on top of a ferroelectric material called aluminum scandium nitride (AlScN), demonstrating for the first time that these two materials can be effectively combined to create transistors at scales attractive to industrial manufacturing.
“Because we have made these devices combining a ferroelectric insulator material with a 2D semiconductor, both are very energy efficient,” says Jariwala. “You can use them for computing as well as memory — interchangeably and with high efficiency.”
The Penn Engineering team’s device is notable for its unprecedented thinness, allowing for each individual device to operate with a minimum amount of surface area. In addition, the tiny devices can be manufactured in large arrays scalable to industrial platforms.
“With our semiconductor, MoS2, at a mere 0.7 nanometers, we weren’t sure it could survive the amount of charge that our ferroelectric material, AlScN, would inject into it,” says Kim. “To our surprise, not only did both of them survive, but the amount of current this enables the semiconductor to carry was also record-breaking.”
The more current a device can carry, the faster it can operate for computing applications. The lower the resistance, the faster the access speed for memory.

This MoS2 and AlScN combination is a true breakthrough in transistor technology. Other research teams’ FE-FETs have been consistently stymied by a loss of ferroelectric properties as devices miniaturize to approach industry-appropriate scales.
Until this study, miniaturizing FE-FETs has resulted in severe shrinking of the “memory window.” This means that as engineers reduce the size of the transistor design, the device develops an unreliable memory, mistaking 1s for 0s and vice versa, compromising its overall performance.
The Jariwala lab and collaborators achieved a design that keeps the memory window large with impressively small device dimensions. With AlScN at 20 nanometers, and MoS2 at 0.7 nanometers, the FE-FET dependably stores data for quick access.
“The key,” says Olsson, “is our ferroelectric material, AlScN. Unlike many ferroelectric materials, it maintains its unique properties even when very thin. In a recent paper from my group, we showed that it can we can retain its unique ferroelectric properties at even smaller thicknesses: 5 nanometers.”
The Penn Engineering team’s next steps are focused on this further miniaturization to produce devices that operate with voltages low enough to be compatible with leading-edge consumer device manufacturing.
“Our FE-FETs are incredibly promising,” says Jariwala. “With further development, these versatile devices could have a place in almost any technology you can think of, especially those that are AI-enabled and consume, generate or process vast amounts of data — from sensing to communications and more.” More

Why do some people take part in genetic studies while others do not? The answer may lie within our genetic makeup. According to a groundbreaking study by Oxford’s Leverhulme Centre for Demographic Science and Big Data Institute, people who participate in genetic studies are genetically more likely to do so, leaving detectable ‘footprints’ in genetics data. This breakthrough equips researchers with the ability to identify and address participation bias, a significant challenge in genetic research.
Stefania Benonisdottir, lead author of the study and a Doctoral candidate from Oxford’s Big Data Institute, explains, ‘Currently, most genetic studies are based on genetic databases which contain large numbers of participants and a wealth of information. However, some people are more likely to be included in these databases than others, which can create a problem called ascertainment bias, where the genetic data collected is not representative of the intended study population.’
To study this link between genetics data and participation bias, the researchers turned to one of the largest biomedical databases in the world, the UK Biobank which contains information from half a million participants.
Using UK Biobank data, it was found there is a genetic component to people’s probability to participate — that is correlated but distinct from other human traits. Published today in Nature Genetics, the study highlights that participation could be an important human trait that has been previously underappreciated and introduces a statistical framework that could lead to more accurate analyses of genetic data.
Professor Augustine Kong, senior author from the Leverhulme Centre for Demographic Science and the Big Data Institute, notes, ‘Ascertainment bias poses a statistical challenge in genetics research, particularly in the era of big data. Adjustments for this bias often rely on known differences between participants and non-participants, introducing imperfections when answering questions involving variables only observed for participants, such as genotypes. Our study identifies detectable footprints of participation bias in the genetic data of participants, which can be exploited statistically to enhance research accuracy for both participants and non-participants alike.’
Genome-wide association studies offer important insights into the role of genetics in human health and diseases. However, such studies can be affected by biases, which arise when genetic databases are not representative of the intended study population. Now, the identified genetic inclination to participate can help scientists assess the representativeness of their study sample.
By analysing the genetic data of over 30,000 related participants with white British descent from the UK Biobank, the researchers found that the genetic component underlying participation in the study is correlated with, but distinct from, the genetic components of traits such as educational attainment and body mass index.
For example, the estimated correlation between the genetic components underlying participation in the UK Biobank and educational attainment is estimated to be 36.6%. This result is consistent with some of the previously reported differences between the participants and the non-participants, but it also shows that the participation bias is not fully captured by these previously known differences. In other words, participation is not simply a consequence of these other traits and characteristics.
The study also found the genetic component of participation can be passed down through families and may affect people’s participation in many different studies over their lifetimes. This highlights the potential for bias in genetic research and underscores the importance of accounting for such biases in study design and analysis.
Professor Melinda Mills, Director of the Leverhulme Centre for Demographic Science concludes, ‘As our GWAS Diversity Monitor shows, the road to improve diversity in genome-wide association studies is long. However, this statistical framework is a huge step in the right direction to mitigate the risk of incomplete or inaccurate data analysis and ensure that genetic research truly benefits everyone.’ More

Routing signals and isolating them against noise and back-reflections are essential in many practical situations in classical communication as well as in quantum processing. In a theory-experimental collaboration, a team led by Andreas Nunnenkamp from the University of Vienna and Ewold Verhagen based at the research institute AMOLF in Amsterdam has achieved unidirectional transport of signals in pairs of “one-way streets.” This research published in Nature Physics opens up new possibilities for more flexible signaling devices.
Devices that allow to route signals, for example carried by light or sound waves, are essential in many practical situations. This is, for instance, the case in quantum information processing, where the states of the quantum computer have to be amplified to read them out — without noise from the amplification process corrupting them. That is why devices that allow signals to travel in a one-way channel e.g. isolators or circulators are much sought-after. However, at present such devices are lossy, bulky, and require large magnetic fields that break time-reversal symmetry to achieve unidirectional behaviour. These limitations have prompted strong efforts to find alternatives that take less space and that do not rely on magnetic fields.
The new study published in Nature Physics introduces a new class of systems characterized by a phenomenon the authors call “quadrature nonreciprocity.” Quadrature nonreciprocity exploits interference between two distinct physical processes. Each of the processes produces a wave that contributes to the transmitted signal. Like water waves produced by two thrown pebbles, the two waves can either cancel or amplify each other, in a phenomenon known as interference.
This allows for unidirectional transmission of signals without time-reversal breaking and leads to a distinctive dependence on the phase, i.e., the quadrature, of the signal. “In these devices, transmission depends not only on the direction of the signal, but also on the signal quadrature” says Clara Wanjura, the theoretical lead author of the study. “This realizes a ‘dual carriageway’ for signals: one quadrature is transmitted in one direction and the other quadrature in the opposite direction. Time-reversal symmetry then enforces that the quadratures always travel pairwise along opposite directions in two separate lanes.”
The experimental team at AMOLF has demonstrated this phenomenon experimentally in a nanomechanical system where interactions among mechanical vibrations of small silicon strings are orchestrated by laser light. Laser light exerts forces on the strings, thereby mediating interactions between their different vibration ‘tones’. Jesse Slim, the experimental lead author of the study says: “We have developed a versatile experimental toolbox that allowed us to control the two different types of interactions that are needed to implement quadrature nonreciprocity. This way we could reveal the resulting unidirectional transport of the signals experimentally.”
The work opens up new possibilities for signal routing and quantum-limited amplification, with potential applications in quantum information processing and sensing. More