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Beyond electronics: Optical system performs feature extraction with unprecedented low latency

Many modern artificial intelligence (AI) applications, such as surgical robotics and real-time financial trading, depend on the ability to quickly extract key features from streams of raw data. This process is currently bottlenecked by traditional digital processors. The physical limits of conventional electronics prevent the reduction in latency and the gains in throughput required in emerging data-intensive services.

The answer to this might lie in harnessing the power of light. Optical computing—or using light to perform demanding computations—has the potential to greatly accelerate feature extraction. In particular, optical diffraction operators, which are plate-like structures that perform calculations as light propagates through them, are highly promising due to their and capacity for parallel processing.

However, pushing these systems to operating speeds beyond 10 GHz in practice remains a technical challenge. This is mainly due to the difficulty of maintaining the stable, coherent light needed for optical computations.

Laser can transform complex semiconductor properties in single-step process

A research team has successfully developed a new technology that converts the conductivity properties of semiconductors with just one laser process.

The research team successfully converted (TiO2), which conventionally works based on electrons, into a hole-based semiconductor. The Laser-Induced Oxidation and Doping Integration (LODI) technology developed by the research team can simultaneously execute oxidation and doping with just one , and it is noted as a novel conversion technology that can drastically streamline the traditional complex process.

The study is published in the journal Small. The team was led by Professor Hyukjun Kwon from the Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology.

All-solid-state battery researchers reveal key insights into degradation mechanisms

Researchers from UNIST, Seoul National University (SNU), and POSTECH have made a significant breakthrough in understanding the degradation mechanisms of all-solid-state batteries (ASSBs), a promising technology for next-generation electric vehicles and large-scale energy storage.

Jointly led by Professor Donghyuk Kim at UNIST’s School of Energy and Chemical Engineering, Professor Sung-Kyun Jung at SNU’s School of Transdisciplinary Innovations, and Professor Jihyun Hong from POSTECH, their study reveals that interfacial chemical reactions play a critical role in structural damage and performance decline in sulfide-based ASSBs. The findings are published in Nature Communications.

Unlike that rely on flammable liquid electrolytes, ASSBs use non-flammable solid electrolytes, offering enhanced safety and higher energy density. However, challenges such as interface instability and microstructural deterioration have impeded their commercialization. Until now, the detailed understanding of how these phenomena occur has remained limited.

Unified memristor-ferroelectric memory developed for energy-efficient training of AI systems

Over the past decades, electronics engineers have developed a wide range of memory devices that can safely and efficiently store increasing amounts of data. However, the different types of devices developed to date come with their own trade-offs, which pose limits on their overall performance and restrict their possible applications.

Researchers at Université Grenoble Alpes (CEA-Leti, CEA List), Université de Bordeaux (CNRS) and Université Paris-Saclay (CNRS) recently developed a new memory device that combines two complementary components typically used individually, known as memristors and ferroelectric capacitors (FeCAPs). This unified memristor-ferroelectric memory, presented in a paper published in Nature Electronics, could be particularly promising for running artificial intelligence (AI) systems that autonomously learn to make increasingly accurate predictions.

“The ‘ideal’ memory would be high-density, non-volatile, capable of non-destructive readout, and offer virtually infinite endurance,” Elisa Vianello, senior author of the paper, told Tech Xplore.

Electric signals reveal magnetic spin waves, hinting at faster computing

Today’s computers store information in magnetic hard drives, keeping files safe even when the device is powered off. But to run programs and process information, computers rely on electricity. Each calculation requires a transfer of information between the electric and magnetic systems. This back-and-forth is a major bottleneck in the speed of modern computing.

Devices that integrate magnetic components directly into computing logic would remove this limitation and allow computers to perform faster and more efficiently.

A new theoretical study led by University of Delaware engineers reveals that magnons, a type of magnetic spin wave, can produce detectable electric signals. The findings, published in the Proceedings of the National Academy of Sciences, highlight potential ways to control and manipulate magnons with electric fields and suggest a path toward integrating electric and magnetic components to enable next-generation computing technologies.

Topological insulator maintains quantum spin Hall effect at higher temperatures

Topological insulators could form the basis for revolutionary electronic components. However, as they generally only function at very low temperatures, their practical application has been severely limited to date. Researchers at the University of Würzburg have now developed a topological insulator that also works at higher temperatures. Their results are published in Science Advances.

A topological insulator can be imagined as a material that is a perfect insulator on the inside—it does not conduct electricity there. At its edges, however, it behaves like an almost lossless “electron highway.” Electrons can move along these paths with almost no loss.

To deepen the analogy: these highways have separate lanes for electrons with different “spins”—a kind of intrinsic angular momentum. Electrons with “spin-up” move in one direction, electrons with “spin-down” in the opposite direction. This strict traffic regulation prevents collisions and thus . The phenomenon behind this is known as the quantum spin Hall effect (QSHE)—an effect that was also first experimentally proven at the University of Würzburg.

New earthquake model goes against the grain

When a slab slides beneath an overriding plate in a subduction zone, the slab takes on a property called anisotropy, meaning its strength is not the same in all directions. Anisotropy is what causes a wooden board to break more easily along the grain than in other directions. In rock, the alignment of minerals such as clay, serpentine, and olivine can lead to anisotropy. Pockets of water in rock can also cause and enhance anisotropy, as repeated dehydration and rehydration commonly occur at depth in a subducting slab.

It is well known that an earthquake generates both a compressional wave and a shear wave. If the shear wave passes through anisotropic rock, it can split into a faster shear wave and a slower one with different polarizations.

Although seismologists routinely measure the shear wave in subduction zones by analyzing recorded seismic waveform data, it is challenging to pinpoint where splitting occurs along the wave propagation path.

Nuclear clock technology enables unprecedented investigation of fine-structure constant stability

In 2024, TU Wien presented the world’s first nuclear clock. Now it has been demonstrated that the technology can also be used to investigate unresolved questions in fundamental physics.

Thorium atomic nuclei can be used for very specific precision measurements. This had been suspected for decades, and the search for suitable atomic nucleus states has been ongoing worldwide. In 2024, a team from TU Wien, with the support of international partners, achieved the decisive breakthrough: the long-discussed nuclear transition was found. Shortly afterward, it was demonstrated that thorium can indeed be used to build high-precision nuclear clocks.

Now, the next major success in high-precision research on thorium nuclei has been achieved: When the thorium nucleus changes between different states, it slightly alters its elliptical shape.

Distributed quantum sensor network achieves ultra-high resolution near Heisenberg limit

Precise metrology forms a fundamental basis for advanced science and technology, including bioimaging, semiconductor defects diagnostics, and space telescope observations. However, the sensor technologies used in metrology have so far faced a physical barrier known as the standard quantum limit.

A promising alternative to surpass this limit is the distributed quantum sensor—a technology that links multiple spatially separated sensors into a single, large-scale quantum system, thereby enabling highly . To date, efforts have primarily focused on enhancing precision, while the potential for extending this approach to has not yet been fully demonstrated.

Dr. Hyang-Tag Lim’s research team at the Center for Quantum Technology, Korea Institute of Science and Technology (KIST), has demonstrated the world’s first ultra-high-resolution distributed quantum sensor network. The study is published in the journal Physical Review Letters.

Mathematical proof unites two puzzling phenomena in spin glass physics

A fundamental link between two counterintuitive phenomena in spin glasses—reentrance and temperature chaos—has been mathematically proven for the first time. By extending the Edwards–Anderson model to include correlated disorder, researchers at Science Tokyo and Tohoku University provided the first rigorous proof that reentrance implies temperature chaos.

Spin glasses are in which atomic “spins,” or tiny magnetic moments, point in random directions rather than aligning neatly as in a regular magnet. These disordered spins can remain stable for extremely long periods of time, possibly even indefinitely. This frozen randomness gives rise to unusual physical properties not seen in any other physical system.

To describe the spin glass behavior, physicists use models such as the Edwards–Anderson (EA) model, which simulates how spins interact in two or three dimensions—conditions that more closely reflect real-world systems than the well-studied mean-field model. Numerical studies of the EA model have uncovered two strange and counterintuitive phenomena: reentrant transitions and temperature .

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