Japanese researchers demo the world's fastest spintronics p-bit

Tohoku University researchers have, for the first time, developed the technology for the nanosecond operation of the spintronics-based probabilistic bit (p-bit) - dubbed the poor man's quantum bit (q-bit).

The late physicist R.P. Feynman envisioned a probabilistic computer: a computer that is capable of dealing with probabilities at scale to enable efficient computing.

"Using spintronics, our latest technology made the first step in realizing Feynman's vision," said Shun Kanai, professor at the Research Institute of Electrical Communication at Tohoku University and lead author of the study.

Magnetic tunnel junctions (MTJs) are the key component of non-volatile memory or MRAM, a mass-produced memory technology that uses magnetization to store information. There, thermal fluctuation typically poses a threat to the stable storage of information.

P-bits, on the other hand, function with these thermal fluctuations in thermally unstable (stochastic) MTJs. Prior collaborative research between Tohoku University and Purdue University demonstrated a spintronics-based probabilistic computer at room temperature consisting of stochastic MTJs with millisecond-long relaxation times.

In order to make probabilistic computers a viable technology, it is necessary to develop stochastic MTJs with much shorter relaxation times which reduces the fluctuation timescale of the p-bit. Doing so would effectively increase the computation speed/accuracy. A top-view scanning electron microscopy image of a magnetic tunnel junction device. © K. Hayakawa et al.

The Tohoku University research group, comprising Kanai, professor Hideo Ohno (the current Tohoku University president), and professor Shunsuke Fukami, produced a nanoscale MTJ device with an in-plane magnetic easy axis (Fig. 1). The magnetization direction updates every 8 nanoseconds on average - 100 times faster than the previous world record (Fig 2).

The group explained the mechanism of this extremely short relaxation time by utilizing entropy - a physical quantity used to represent the stochasticity of systems that had previously not been considered for magnetization dynamics. Deriving a universal equation governing the entropy in magnetization dynamics, they discovered that the entropy rapidly increases in MTJs with an in-plane easy axis with larger magnitudes of perpendicular magnetic anisotropy. The group intentionally employed an in-plane magnetic easy axis for achieving shorter relaxation times.

"The developed MTJ is compatible with current semiconductor back-end-of-line processes and shows substantial promise for the future realization of high-performance probabilistic computers," added Kanai. "Our theoretical framework of magnetization dynamics including entropy also has a broad scientific implication, ultimately showing the potential of spintronics to contribute to debatable issues in statistical physics." Real time measured transmitted voltage signal which reflects the magnetization state as well as bit state. Relaxation time, defined as a switching time averaging over 100 million times a second, was observed. ©K. Hayakawa et al.

UVA, Northwestern engineers help keep pace with Moore's Law by exploring a new material class

University of Virginia School of Engineering and Northwestern University researchers create a new polymer-based electrical insulation for circuits that could help put more power in smaller spaces

Progress in the field of integrated circuits is measured by matching, exceeding, or falling behind the rate set forth by Gordon Moore, former CEO and co-founder of Intel, who said the number of electronic components, or transistors, per integrated circuit would double every year. That was more than 50 years ago, and surprisingly his prediction, now called Moore's Law, came true.

In recent years, it was thought that the pace had slowed; one of the biggest challenges of putting more circuits and power on a smaller chip is managing heat.

A multidisciplinary group that includes Patrick E. Hopkins, a professor in the University of Virginia's Department of Mechanical and Aerospace Engineering, and Will Dichtel, a professor in Northwestern University's Department of Chemistry, is inventing a new class of material with the potential to keep chips cool as they keep shrinking in size -- and to help Moore's Law remain true. Their work was recently published in an academic journal. Impedance measurements conducted on parallel plate capacitors confirm that COF-5 is a low-k dielectric.  CREDIT Austin Evans

Electrical insulation materials that minimize electrical crosstalk in chips are called "low-k" dielectrics. This material type is the silent hero that makes all electronics possible by steering the current to eliminate signal erosion and interference; ideally, it can also pull damaging heat caused by electrical current away from the circuitry. The heat problem becomes exponential as the chip gets smaller because not only are there more transistors in a given area, which makes more heat in that same area, they are closer together, which makes it harder for heat to dissipate.

"Scientists have been in search of a low-k dielectric material that can handle the heat transfer and space issues inherent at much smaller scales," Hopkins said. "Although we've come a long way, new breakthroughs are just not going to happen unless we combine disciplines. For this project, we've used research and principles from several fields - mechanical engineering, chemistry, materials science, electrical engineering -- to solve a really tough problem that none of us could work out on our own."

Hopkins is one of the leaders of UVA Engineering's Multifunctional Materials Integration initiative, which brings together researchers from multiple engineering disciplines to formulate materials with a wide array of functionalities.

"Seeing 'my' problem through someone else's lens in a different field was not only fascinating, but it also sparked ideas that ultimately brought advancement. I think we all had that experience," said Ashutosh Giri, a former UVA Engineering senior scientist and Ph.D. student in Hopkins' lab, the co-first author on the Nature Materials paper, and a mechanical, industrial, and systems engineering assistant professor at Rhode Island University.

"The heart of the project was when the chemical team realized the thermal functionality of their material, understanding a new dimension about their work, and when the mechanical and materials team understood the level of molecular engineering possible with chemistry," Giri said.

"We're taking sheets of polymer that are only one atom thick - we call this 2D - and controlling their properties by layering the sheets in a specific architecture," Dichtel said.

"Our efforts on improving the methods to produce high-quality 2D polymer films enabled this collaborative work."

The team is applying this new material class to try to meet the requirements of miniaturizing transistors on a dense chip, Dichtel said.

"This has enormous potential for use in the semiconductor industry, the industry that manufactures chips. The material has both low electrical conductivity, or 'low-k,' and high heat transfer capability," he said.

This combination of properties was recently identified by the International Roadmap for Semiconductors as a prerequisite for next-generation integrated circuits.

"For this project, we are focusing on the thermal properties of this new material class, which is fantastic, but even more exciting is that we are just scratching the surface," said Austin Evans, a Ph.D. student in Dichtel's lab at Northwestern and first co-author on the paper. "Developing new classes of materials with unique combinations of properties has amazing technological potential.

"We are already exploring this new class of materials for many applications, for instance, chemical sensing. We can use these materials to determine -- 'sense' -- what chemicals and how much of those chemicals are in the air. This has broad-reaching implications. For instance, by knowing about the chemicals in the air, we can optimize food storage, transport, and distribution to reduce global food waste. As we continue exploring, we are likely to find even more traits unique to these new materials," Evans said.

Researchers from Queen Mary University of London develop a new computational approach to predict how liquids freeze

The process of freezing, where a liquid turns into a solid, isn’t as simple as it might seem. Many substances, including water and wax, have several solid states as a result of differences in the arrangement of their atoms and molecules. However, performing experiments to visualize the exact molecular arrangements and how they transform between states can be difficult.

Over the last few decades, computational models have increasingly been used to complement experimental studies, bringing new molecular insights into the properties of gas and liquid states as well as the transitions between them (e.g. evaporation).

However denser phases are still a challenge, and the complexity of the freezing liquids into solids has eluded most methods, especially where there is more than one possible solid arrangement. Icicles hanging from a pipe. Credit: Besjunior/iStock.com.

In the study, published in the Journal of Physical Chemistry B, the scientists developed novel computational approaches to study wax, which is known to have multiple frozen arrangements. Using their method they were able to predict its melting point within 2°C of the experimental value.

Comparing performance

When they compared the performance of these methods with most existing computational techniques, they showed their modeling approach provided a more realistic view of what happens when liquids freeze and could even predict some of the more ‘exotic’ crystal structures formed during this process.

Dr. Stephen Burrows, Postdoctoral Research Assistant at Queen Mary, said: “Solid alkanes are unusual because the molecules have a surprising amount of freedom. If you start from a perfect crystal and increase the temperature, the molecules suddenly gain the ability to rotate, with a motion similar to a restless sleeper tossing and turning in bed.”

“We have tested the most widely used methods to simulate these ‘rotator’ phases, finding that the Williams model from the 1960s was ahead of its time. Initially impractical due to a lack of computational power, it may now undergo a renaissance for modern molecular dynamics simulation. With our newly optimized model, we aim to study the rotator phase of hexadecane, found in the oil, which is hard to observe experimentally because of its unstable nature.”

Real-world applications

Like waxes, oils such as diesel fuel can also freeze at many stages and exhibit different solid properties. Therefore, methods to predict the molecular and atomic intricacies of liquid transitions to different types of ‘solid’ oils could have several potential real-world applications, from helping better predict freezing of oil pipelines (and preventing oil spills), to developing better smart insulation and energy storage.

Understanding solid transitions in wax could also lead to lighter, stronger-than-steel polymers, and help researchers to improve understanding of newly discovered processes like artificial morphogenesis. These could enable greener manufacturing processes so we could ‘grow’ matter as seen in nature, reducing side or waste products.

Dr. Stoyan Smoukov, Reader in Chemical Engineering at Queen Mary, said: “Being able to predict the transformation behavior of oils would help us in our quest to develop sustainable manufacturing processes for the future. Usual lithographic microfabrication is like sculpturing, cutting/chiseling away from a slab of marble, generating lots of waste. In our current grant, we are using novel processes to self-shape droplets and use nearly 100% of the starting material to literally grow shaped particles.”

“The process is highly scalable as each droplet shapes itself due to internal phase transitions. The efficient production of such particles could revolutionize industries from inkjet printing to drug delivery. And the modeling tools we’ve developed will help us tune this control on the molecular scale.”