A quick electric pulse completely flips the material’s electronic properties, opening a route to ultrafast, brain-inspired, superconducting electronics.

With some careful twisting and stacking, MIT physicists have revealed a new and exotic property in “magic-angle” graphene: superconductivity that can be turned on and off with an electric pulse, much like a light switch.

The discovery could lead to ultrafast, energy-efficient superconducting transistors for neuromorphic devices — electronics designed to operate in a way similar to the rapid on/off firing of neurons in the human brain.

Magic-angle graphene refers to a very particular stacking of graphene — an atom-thin material made from carbon atoms that are linked in a hexagonal pattern resembling chicken wire. When one sheet of graphene is stacked atop a second sheet at a precise “magic” angle, the twisted structure creates a slightly offset “moiré” pattern or superlattice, that can support a host of surprising electronic behaviors.

In 2018, Pablo Jarillo-Herrero and his group at MIT were the first to demonstrate magic-angle twisted bilayer graphene. They showed that the new bilayer structure could behave as an insulator, much like wood, when they applied a certain continuous electric field. When they upped the field, the insulator suddenly morphed into a superconductor, allowing electrons to flow, friction-free.

That discovery was a watershed in the field of “twistronics,” which explores how certain electronic properties emerge from the twisting and layering of two-dimensional materials. Researchers including Jarillo-Herrero have continued to reveal surprising properties in magic-angle graphene, including various ways to switch the material between different electronic states. So far, such “switches” have acted more like dimmers, in that researchers must continuously apply an electric or magnetic field to turn on superconductivity and keep it on.

Now Jarillo-Herrero and his team have shown that superconductivity in magic-angle graphene can be switched on, and kept on, with just a short pulse rather than a continuous electric field. The key, they found, was a combination of twisting and stacking.

The team reports that by stacking magic-angle graphene between two offset layers of boron nitride — a two-dimensional insulating material — the unique alignment of the sandwich structure enabled the researchers to turn graphene’s superconductivity on and off with a short electric pulse.

“For the vast majority of materials, if you remove the electric field, zzzzip, the electric state is gone,” says Jarillo-Herrero, who is the Cecil and Ida Green Professor of Physics at MIT. “This is the first time that a superconducting material has been made that can be electrically switched on and off, abruptly. This could pave the way for a new generation of twisted, graphene-based superconducting electronics.”

His MIT co-writers are lead researcher Dahlia Klein Ph.D. ’21, graduate student Li-Qiao Xia, and former postdoc David MacNeill, along with Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan.

Flipping the switch

In 2019, a team at Stanford University discovered that magic-angle graphene could be coerced into a ferromagnetic state. Ferromagnets are materials that retain their magnetic properties, even in the absence of an externally applied magnetic field.

The researchers found that magic-angle graphene could exhibit ferromagnetic properties in a way that could be turned on and off. This happened when the graphene sheets were layered between two sheets of boron nitride such that the crystal structure of the graphene was aligned to one of the boron nitride layers. The arrangement resembled a cheese sandwich in which the top slice of bread and the cheese orientations are aligned, but the bottom slice of bread is rotated at a random angle with respect to the top slice. The result intrigued the MIT group.

“We were trying to get a stronger magnet by aligning both slices,” Jarillo-Herrero says. “Instead, we found something completely different.”

In their current study, the team fabricated a sandwich of carefully angled and stacked materials. The “cheese” of the sandwich consisted of magic-angle graphene — two graphene sheets, the top rotated slightly at the “magic” angle of 1.1 degrees with respect to the bottom sheet. Above this structure, they placed a layer of boron nitride, exactly aligned with the top graphene sheet. Finally, they placed a second layer of boron nitride below the entire structure and offset it by 30 degrees with respect to the top layer of boron nitride.

The team then measured the electrical resistance of the graphene layers as they applied a gate voltage. They found as others have, that the twisted bilayer graphene switched electronic states, changing between insulating, conducting, and superconducting states at certain known voltages.

What the group did not expect was that each electronic state persisted rather than immediately disappearing once the voltage was removed — a property known as bistability. They found that, at a particular voltage, the graphene layers turned into a superconductor, and remained superconducting, even as the researchers removed this voltage.  

This bistable effect suggests that superconductivity can be turned on and off with short electric pulses rather than a continuous electric field, similar to flicking a light switch. It isn’t clear what enables this switchable superconductivity, though the researchers suspect it has something to do with the special alignment of the twisted graphene to both boron nitride layers, which enables a ferroelectric-like response of the system. (Ferroelectric materials display bistability in their electric properties.)

“By paying attention to the stacking, you could add another tuning knob to the growing complexity of magic-angle, superconducting devices,” Klein says. 

For now, the team sees the new superconducting switch as another tool researchers can consider as they develop materials for faster, smaller, more energy-efficient electronics.

“People are trying to build electronic devices that do calculations in a way that’s inspired by the brain,” Jarillo-Herrero says. “In the brain, we have neurons that, beyond a certain threshold, they fire. Similarly, we now have found a way for magic-angle graphene to switch superconductivity abruptly, beyond a certain threshold. This is a key property in realizing neuromorphic computing.”  

This research was supported in part by the U.S. Air Force Office of Scientific Research, the U.S. Army Research Office, and the Gordon and Betty Moore Foundation.

When pondering the probability of discovering technologically advanced extraterrestrial life, the question that often arises is, "if they're out there, why haven't we found them yet?" And often, the response is that we have only searched a tiny portion of the galaxy. Further, algorithms developed decades ago for the earliest digital computers can be outdated and inefficient when applied to modern petabyte-scale datasets. Now, research led by an undergraduate student at the University of Toronto, Peter Ma, along with researchers from the SETI Institute, Breakthrough Listen, and scientific research institutions around the world, has applied a deep learning technique to a previously studied dataset of nearby stars and uncovered eight previously unidentified signals of interest.

“In total, we had searched through 150 TB of data of 820 nearby stars, on a dataset that had previously been searched through in 2017 by classical techniques but labeled as devoid of interesting signals," said Peter Ma, lead author. “We're scaling this search effort to 1 million stars today with the MeerKAT telescope and beyond. We believe that work like this will help accelerate the rate we’re able to make discoveries in our grand effort to answer the question ‘are we alone in the universe?’”

The search for extraterrestrial intelligence (SETI) looks for evidence of extraterrestrial intelligence originating beyond Earth by trying to detect technosignatures, or evidence of technology, that alien civilizations could have developed. The most common technique is to search for radio signals. Radio is a great way to send information over the incredible distances between the stars; it quickly passes through the dust and gas that permeate space, and it does so at the speed of light (about 20,000 times faster than our best rockets). Many SETI efforts use antennas to eavesdrop on any radio signals aliens might be transmitting.

This study re-examined data taken with the Green Bank Telescope in West Virginia as part of a Breakthrough Listen campaign that initially indicated no targets of interest. The goal was to apply new deep learning techniques to a classical search algorithm to yield faster, more accurate results. After running the new algorithm and manually re-examining the data to confirm the results, newly detected signals had several key characteristics:

1. The signals were narrow band, meaning they had narrow spectral width, on the order of just a few Hz. Signals caused by natural phenomena tend to be broadband.
2. The signals had non-zero drift rates, which means the signals had a slope. Such slopes could indicate a signal’s origin had some relative acceleration with our receivers, hence not local to the radio observatory.
3. The signals appeared in ON-source observations and not in OFF-source observations. If a signal originates from a specific celestial source, it appears when we point our telescope toward the target and disappears when we look away. Human radio interference usually occurs in ON and OFF observations due to the source being close by.

Cherry Ng, another of Ma’s research advisors and an astronomer at both the SETI Institute and the French National Center for Scientific Research said, “These results dramatically illustrate the power of applying modern machine learning and computer vision methods to data challenges in astronomy, resulting in both new detections and higher performance. Application of these techniques at scale will be transformational for radio techno signature science.”

While re-examinations of these new targets of interest have yet to result in re-detections of these signals, this new approach to analyzing data can enable researchers to more effectively understand the data they collect and act quickly to re-examine targets.  Ma and his advisor Dr. Cherry Ng are looking forward to deploying extensions of this algorithm on the SETI Institute’s COSMIC system.

Since SETI experiments began in 1960 with Frank Drake’s Project Ozma at the Greenbank Observatory, a site now home to the telescope used in this latest work, technological advances have enabled researchers to collect more data than ever. This massive volume of data requires new computational tools to process and analyze that data quickly to identify anomalies that could be evidence of extraterrestrial intelligence. This new machine-learning approach is breaking new ground in the quest to answer the question, “are we alone?”

Intel has reported terrible fourth-quarter sales, and the year-over-year (YoY) comparisons are just as painful.

Fourth-quarter revenue was $14 billion, down 32% YoY, and $63.1 billion for the full year 2022, down 20% YoY. 

Its data center and AI (DCAI) sales of $4.3 billion have plunged by 33% in the quarter. 

The company's net income was painful; down 114% in the quarter and down 60% to $8 billion for the year. 

As a result, Intel shares closed 6.4% lower today. It saw $8 billion wiped off its market value after it baffled Wall Street with dismal earnings projections. They have predicted a surprise loss for the first quarter, and its revenue forecast was $3 billion below estimates as it struggled with growing the data center business.

Intel previously announced several organizational changes to accelerate its execution and innovation by allowing it to capture growth in both large traditional markets and high-growth emerging markets. This includes the reorganization of Intel's business units to capture this growth and provide increased transparency, focus, and accountability. As a result, the company modified its segment reporting in the first quarter of 2022 to align with the previously announced business reorganization. All prior-period segment data has been retrospectively adjusted to reflect the way the company internally manages and monitors operating segment performance starting in the fiscal year 2022.

“Despite the economic and market headwinds, we continued to make good progress on our strategic transformation in Q4, including advancing our product roadmap and improving our operational structure and processes to drive efficiencies while delivering at the low end of our guided range,” said Pat Gelsinger, Intel CEO. “In 2023, we will continue to navigate the short-term challenges while striving to meet our long-term commitments, including delivering leadership products anchored on open and secure platforms, powered by at-scale manufacturing and supercharged by our incredible team.” 

“In the fourth quarter, we took steps to right-size the organization and rationalize our investments, prioritizing the areas where we can deliver the highest value for the long term,” said David Zinsner, Intel CFO. “These actions underpin our cost-reduction targets of $3 billion in 2023, and set the stage to achieve $8 billion to $10 billion by the end of 2025.”

A Northumbria University physicist has been awarded more than half a million pounds to develop artificial intelligence which will protect the Earth from devastating space storms.

Activity from the Sun such as solar eruptions, known as Coronal Mass Ejections, results in plasma being fired toward Earth at supersonic speeds, which can result in serious disruption to power and communication systems.

With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011.

Northumbria’s Dr. Andy Smith has recently been awarded a Research Fellowship from the Natural Environment Research Council (NERC) to explore how physics-inspired machine learning could be used to forecast space weather more accurately and predict serious space storms.

During the Next Generation, Physics-Inspired AI for Space Weather Forecasting project, Dr. Smith and his team will analyze huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.

They will then develop cutting-edge supercomputer models which will use the data gathered to predict when such storms could occur in the future, forecasting phenomena such as the northern lights, or aurora.

As Dr. Smith explains: “One of the primary ways in which space weather can impact society is through an unexpected surge of energy in power networks and pipelines on the ground.

“These surges can accelerate the aging of power systems, or even lead to the immediate failure of components such as power transformers, leading to a complete loss of power.

“This research will take a leap forward in understanding and predicting when we are at risk of suffering from these surges, caused by rapid changes in the Earth's magnetic field.”

Throughout history, there have been several examples of serious geomagnetic space storms. In March 1989 the Canadian city of Quebec lost power for over nine hours following a huge solar storm, resulting in auroras or ‘polar lights’ being visible as far south as Texas and Florida.

And in 2003 the Halloween solar storms, named because they occurred at the end of October, affected satellite-based systems and communications, with aircraft being advised to avoid high altitudes near the polar regions and an hour-long power outage in Sweden.

But the most intense geomagnetic storm ever recorded was the 1859 Carrington Event, which resulted in strong auroral displays visible around the world, as well as fires in multiple telegraph stations. The solar flare connected with the event was observed and recorded independently by British astronomers Richard Christopher Carrington and Richard Hodgson.

As Dr. Smith explains: “Our reliance on electrical power networks means that a storm on the same scale as the Carrington Event would have devastating consequences today, making an accurate forecasting system even more essential.

“The technology we are developing through this project could protect the Earth from the impact of geomagnetic storms as we could predict when such events would occur, allowing us to prepare.

“For example, in the UK this would be coordinated through the Met Office which would inform the National Grid, which would in turn activate plans to protect our power grid.

“It’s not a case of if the Earth will be hit by a serious space weather event, it’s a case of when – and this physics-inspired artificial intelligence system will allow us to predict such an event and protect ourselves from it.”

Dr. Smith is a member of Northumbria University’s Solar and Space Physics research group. This is the latest in a series of high-profile grants awarded to academics at the University studying the impact of space weather on the Earth.

In 2021 a team led by Professor Clare Watt was awarded £400,000 from the Science and Technology Facilities Council (STFC) to develop new methods of predicting conditions in the radiation belts above the Earth, providing safer conditions for satellites and spacecraft.

Dr. Shaun Bloomfield led Northumbria's involvement in the Space Weather Empirical Ensemble Package (SWEEP) project, commissioned by the Met Office to develop an improved system for forecasting solar storms. And he was also a Project Scientist in the FLARECAST project, which involved scientists from six countries developing a service to predict the occurrence of solar flares.

Dr. Richard Morton is leading the £ 1.2 million Revealing the Pattern of Solar Alfvénic Waves (RiPSAW) project, having been awarded a prestigious UKRI Future Leader Fellowship in 2020. The project involves using advanced mathematical techniques and cutting-edge computer simulations to create models of the Sun which will provide new insight into the physics behind its activity.

Professor James McLaughlin leads Northumbria University’s Solar and Space Physics research group and is the Principal Investigator of the £ 1.3 million NUdata STFC Centre for Doctoral Training in Data Intensive Science.

He said: “Northumbria University plays multiple, key roles in the UK’s endeavor to understand the scientific and technical aspects of Space Weather. And via our Centre for Doctoral Training in Data Intensive Science, Northumbria is training the next generation of data science and artificial intelligence specialists. Dr. Smith’s new project complements and enhances both these areas of University strength.”