A new specialized, radiation-hardened chip has been designed for CERN’s Large Hadron Collider (LHC) upgrade.
Engineers at Columbia University have developed this analog-to-digital converter (ADC) chip.
The custom-designed chips will be used in the ATLAS detector to measure up to 1.5 billion particle collisions per second.
In nanoscale transistors, quantum mechanical effects such as tunneling and quantization significantly influence device characteristics. However, large-scale quantum transport simulation remains a challenging field, making it difficult to account for quantum mechanical effects arising from the complex device geometries. Here, based on large-scale quantum transport simulations, we demonstrate that quantum geometrical effects in stacked nanosheet GAAFETs significantly impact carrier injection characteristics. Discontinuities in confinement energy at the constriction—the junction between the bulk source/drain and nanosheet channel—cause substantial carrier backscattering. This degradation becomes more severe as electrons experience higher effective energy barriers, and is further exacerbated at lower scattering rate, lower doping concentrations, and near Schottky barriers where electron depletion regions form. Considering these quantum mechanical bottlenecks, proper device optimization for future technology nodes requires a full quantum-based device structure design at the large-scale level, which enables unique optimization strategies beyond conventional classical prediction.
Kyoung Yeon Kim and colleagues report the importance of quantum geometrical effects that serve as a bottleneck in stacked nanosheet GAAFETs. This highlights that full quantum mechanics-based device design is crucial for realizing ideal carrier injection characteristics in future technology nodes.
Researchers with the schools of science and engineering at Rensselaer Polytechnic Institute (RPI) are exploring new ways to manipulate matter with light to unlock a new generation of computer chips, photovoltaic cells and other advanced materials.
Physics professor Moussa N’Gom, Ph.D., and materials science professor Edwin Fohtung, Ph.D., have brought together their respective areas of expertise—optics and materials science —to illuminate previously unknown properties of the materials that will build the next generation of consumer, industrial and scientific devices.
“We can use almost the entire spectrum of light, from visible to X-ray, to manipulate and study materials,” Fohtung said. “We can interrogate any system, from hard condensed matter to soft biological tissue.”
Healthy brain function relies on a steady supply of blood. Disruptions in blood flow are linked to major neurological conditions like stroke, Alzheimer’s disease (AD), and traumatic brain injuries. But understanding how the brain fine-tunes this flow—especially across its smallest blood vessels—remains a challenge.
The brain’s blood supply includes a vast network of vessels, ranging from large arteries to microscopic capillaries. Between these lie transitional zone (TZ) vessels—such as penetrating arterioles, precapillary arterioles, and capillary sphincters—that bridge the gap and may play a big role in regulating flow. But their exact contribution, particularly during increased brain activity, remains a subject of scientific debate.
To explore these dynamics, researchers from the College of Engineering and Computer Science at Florida Atlantic University and the FAU Sensing Institute (I-SENSE) developed a highly detailed computer model of the mouse brain’s vasculature, treating each vessel segment as a tiny, adjustable valve.
Gradient-boosted decision trees (GBDTs) power everything from real-time fraud filters to petabyte-scale demand forecasts. XGBoost open source library has long been the tool of choice thanks to state-of-the-art accuracy, SHAP-ready explainability, and flexibility to run on laptops, multi-GPU nodes, or Spark clusters. XGBoost version 3.0 was developed with scalability as its north star. A single NVIDIA GH200 Grace Hopper Superchip can now process datasets from gigabyte scale all the way to 1 terabyte (TB) scale.
The coherent memory architecture allows the new external-memory engine to stream data over the 900 GB/s NVIDIA NVLink-C2C, so a 1 TB model can be trained in minutes—up to 8x faster than a 112-core (dual socket) CPU box. This reduces the need for complex multinode GPU clusters, and makes scalability simpler to achieve.
This post explains new features and enhancements in the milestone XGBoost 3.0 release, including a deep dive into external memory and how it leverages the Grace Hopper Superchip to reach 1 TB scale.
Gallium nitride (GaN)-based high electron mobility transistors (HEMTs) are a type of field-effect transistors (FETs) designed to operate at very high frequencies with low noise. As such, they have been widely applied in high-power and high-frequency applications, like high-speed wireless communications, power switching devices, and power amplifiers.
HEMTs utilize a heterojunction, which is a junction between two different semiconductor materials, typically GaN and aluminum GaN (AlGaN). This junction creates a narrow region called the two-dimensional electron gas (2DEG), where electrons have very high mobility, resulting in excellent high-frequency performance.
Scandium aluminum nitride (ScAlN) has attracted significant attention as a novel barrier material that can further enhance the performance of GaN HEMTs. It exhibits large polarization, which increases electron densities in the 2DEG. Additionally, its ferroelectric nature makes it suitable for use as a ferroelectric gate material in ferroelectric HEMTs.
Just as overlapping ripples on a pond can amplify or cancel each other out, waves of many kinds—including light, sound and atomic vibrations—can interfere with one another. At the quantum level, this kind of interference powers high-precision sensors and could be harnessed for quantum computing.
In a new study published in Science Advances, researchers at Rice University and collaborators have demonstrated a strong form of interference between phonons—the vibrations in a material’s structure that constitute the tiniest units (quanta) of heat or sound in that system. The phenomenon where two phonons with different frequency distributions interfere with each other, known as Fano resonance, was two orders of magnitude greater than any previously reported.
“While this phenomenon is well-studied for particles like electrons and photons, interference between phonons has been much less explored,” said Kunyan Zhang, a former postdoctoral researcher at Rice and first author on the study. “That is a missed opportunity, since phonons can maintain their wave behavior for a long time, making them promising for stable, high-performance devices.”
A photonics research group co-led by Gregor Weihs of the University of Innsbruck has developed a new technique for generating multi-photon states from quantum dots that overcomes the limitations of conventional approaches. This has immediate applications in secure quantum key distribution protocols, where it can enable simultaneous secure communication with different parties.
Quantum dots—semiconductor nanostructures that can emit single photons on demand—are considered among the most promising sources for photonic quantum computing. However, every quantum dot is slightly different and may emit a slightly different color. This means that to produce multi-photon states, we cannot use multiple quantum dots.
Usually, researchers use a single quantum dot and multiplex the emission into different spatial and temporal modes, using a fast electro-optic modulator. The technological challenge is that faster electro-optic modulators are expensive and often require very customized engineering. To add to that, they may not be very efficient, which introduces unwanted losses into the system.