Tyler O'Neal, Staff Editor ACADEMIA

Russian scientists study atomic structure of aluminum alloys for manufacturing modern aircraft

Researchers from the Belgorod State University (BSU) and the Skolkovo Institute of Science and Technology (Skoltech) in Russia have studied aluminum alloys at the atomic level and found patterns that will help improve their structure. The findings will be useful for developing new alloys for modern aircraft.

According to Marat Gazizov, senior researcher at the BSU Laboratory of Mechanical Properties of Nanostructured and Heat-Resistant Materials, the study focused on the Al-Cu-Mg-Ag system used for the wing and fuselage skin. The aluminum alloys used in aircraft structures have a wealth of advantages, such as small weight and resistance to wear and fracture at elevated temperatures, as well as cyclic and shock loads.

"Aluminum is combined with copper (Cu), magnesium (Mg), silver (Ag) and some other elements to achieve the desired properties. This process called alloying can significantly enhance the strength of the material treated by specific thermal or thermomechanical methods," Marat Gazizov explains.

Al-Cu-Mg-Ag alloying helps obtain high heat resistance alloys, but according to the project lead, the evolution of the alloy's structure and mechanical properties in various thermal or thermomechanical treatment modes and operating conditions is still not well understood, which explains the choice of topic for this study.

Marat Gazizov adds that the alloys are used as a structural material for parts and assemblies exposed to elevated temperatures, which calls for a unique combination of strength, fracture toughness, and high fatigue crack growth resistance.

"These days, computer simulation is no longer viewed as a 'magic wand' and is commonly used to study atomic-level effects. While experimenting with the heat-resistant aluminum alloy containing very small quantities of copper, magnesium and silver, we observed the formation of dispersed particles with a thickness of only a few nanometers which make the alloy much stronger despite their small size. In addition, the particles turned out to be coherent and fit well into the aluminum matrix, like pieces of a puzzle, although with slight distortions in their atomic structure. Also, we found that the particles' structure and, therefore, the heat-treated alloy's mechanical behavior change according to a certain pattern," Anton Boev, a research scientist at Skoltech, notes.

The study enhances the understanding of the unique mechanical properties and structure of aluminum alloys. The combination of mechanical properties obtained by the team will help extend the lifetime of aircraft structures made from these materials.

Chinese astronomers reveal a lighter Milky Way based on Gaia EDR3 data

Tyler O'Neal, Staff Editor ACADEMIA

The mass of the Milky Way is a fundamental quantity in modern astrophysics and cosmology that has a direct impact on many astrophysical problems.

With the combination of high-precision data from Gaia EDR3 and a new-generation dynamical modeling method, an international research team has found that the total mass of the Milky Way ranges from 500–800 billion solar mass, which indicates a lighter Milky Way when compared to previous measurements.

The study was led by Chinese astronomer WANG Jianling from the National Astronomical Observatories of the Chinese Academy of Sciences (NAOC), in collaboration with Francois Hammer and YANG Yanbin from the Paris Observatory under the framework of the Sino-French collaborative "Tianguan" project.

Previous studies of galactic dynamics were affected by two factors: Either they were based on a too small data set that introduced large uncertainties, or the tracers they used lacked information. The latter could be a serious matter since simple hypotheses on the equilibrium of some distant tracers have been used, thus introducing unknown systematic problems.

Now we are entering a golden era of galactic archeology with progress on large-scale spectroscopic surveys and high-precision proper motion measurements from the Gaia satellite, which provides a huge amount of high-quality data. These data overcome many difficulties mentioned above, especially by providing full, six-dimensional information for high-precision tracers acquired by Gaia.

Using these unprecedented data to study how our Milky Way and its halo are structured and how they assembled together is the central task facing astronomers, and dynamic modeling with supercomputers is the central tool for accomplishing this task.

The astronomers used a new-generation, analytical dynamic modeling technique, i.e., action-based, distribution function dynamical modeling. They derived the Milky Way baryon mass and dark matter mass distribution function, which in turn provided the accurate total mass of the Milky Way.

Thanks to the precise proper motions of Gaia, they derived the most precise kinematic information for around 150 galactic globular clusters. They combined this information with the accurate rotation curve information from the disk region also based on Gaia data. The flexible action-based distribution function overcomes many simplistic assumptions adopted by previous studies, thus leading to a more realistic distribution function for the tracers, and to a Milky Way mass distribution function.

N-body simulation and realistic cosmological hydrodynamic simulations have been used in this work to quantify any systematics introduced by the Large Magellanic Cloud passing by as well as by unrelaxed substructures.

This study has significant implications for cosmological problems and the origin of Milky Way satellites.

SAIT demos the world's first MRAM based in-memory computing

Tyler O'Neal, Staff Editor ACADEMIA

Samsung Electronics has announced its demonstration of the world's first in-memory computing based on MRAM (Magnetoresistive Random Access Memory). This research showcases Samsung’s leadership in-memory technology and its effort to merge memory and system semiconductors for next-generation artificial intelligence (AI) chips.

The research was led by Samsung Advanced Institute of Technology (SAIT) in close collaboration with Samsung Electronics Foundry Business and Semiconductor R&D Center. The researcher, Dr. Seungchul Jung, Staff Researcher at SAIT, and the co-corresponding scientists Dr. Donhee Ham, Fellow of SAIT and Professor of Harvard University, and Dr. Sang Joon Kim, Vice President of Technology at SAIT, spearheaded the research.

In the standard computer architecture, data is stored in memory chips and data computing is executed in separate processor chips. In contrast, in-memory computing is a new computing paradigm that seeks to perform both data storage and data computing in a memory network. Since this scheme can process a large amount of data stored within the memory network itself without having to move the data, and also because the data processing in the memory network is executed in a highly parallel manner, power consumption is substantially reduced. In-memory computing has thus emerged as one of the promising technologies to realize next-generation low-power AI semiconductor chips.

For this reason, research on in-memory computing has been intensely pursued worldwide. Non-volatile memories, in particular RRAM (Resistive Random Access Memory) and PRAM (Phase-change Random Access Memory), have been actively used for demonstrating in-memory computing. By contrast, it has so far been difficult to use MRAM ─ another type of non-volatile memory ─ for in-memory computing despite MRAM’s merits such as operation speed, endurance, and large-scale production. This difficulty stems from the low resistance of MRAM, due to which MRAM cannot enjoy the power reduction advantage when used in the standard in-memory computing architecture.

The Samsung Electronics researchers have provided a solution to this issue by an architectural innovation. Concretely, they succeeded in developing an MRAM array chip that demonstrates in-memory computing, by replacing the standard, current-sum in-memory computing architecture with a new, ‘resistance sum’ in-memory computing architecture, which addresses the problem of small resistances of individual MRAM devices.

Samsung’s research team subsequently tested the performance of this MRAM in-memory computing chip by running it to perform AI computing. The chip achieved an accuracy of 98% in the classification of hand-written digits and a 93% accuracy in detecting faces from scenes.

By ushering MRAM ─ the memory which has already reached commercial-scale production embedded in the system semiconductor fabrication ─ into the realm of in-memory computing, this work expands the frontier of the next-generation low-power AI chip technologies.

The researchers have also suggested that not only can this new MRAM chip be used for in-memory computing, but it also can serve as a platform to download biological neuronal networks. This is along the line of the neuromorphic electronics vision that Samsung’s researchers recently put forward in a perspective paper.

"In-memory computing draws similarity to the brain in the sense that in the brain, computing also occurs within the network of biological memories, or synapses, the points where neurons touch one another,” said Dr. Seungchul Jung, the first author of the paper. “While the computing performed by our MRAM network, for now, has a different purpose from the computing performed by the brain, such solid-state memory network may in the future be used as a platform to mimic the brain by modeling the brain’s synapse connectivity."

As highlighted in this work, by building on its leading memory technology and merging it with system semiconductor technology, Samsung plans to continue to expand its leadership in next-generation supercomputing and AI semiconductors.