Category Physics

New research from the WISE group (Wearable, Intelligent, Soft Electronics) at The University of Hong Kong (HKU-WISE) has addressed a long-standing bioelectronic challenge: the development of soft, 3D transistors.

This work introduces a new approach to semiconductor device design with transformative potential for bioelectronics. It is published in Science.

Led by Professor Shiming Zhang from the Department of Electrical and Electronic Engineering, Faculty of Engineering, the research team included senior researchers who joined HKU-WISE from the University of Cambridge and the University of Chicago, together with HKU Ph.D...

At TU Wien, researchers have discovered a state in a quantum material that had previously been considered impossible. The definition of topological states should be generalized.

The work is published in Nature Physics.

Quantum physics tells us that particles behave like waves and, therefore, their position in space is unknown. Yet in many situations, it still works remarkably well to think of particles in a classical way—as tiny objects that move from place to place with a certain velocity.

When physicists describe how electric current flows through metals, for example, they imagine electrons racing through the material and being accelerated or deflected by electromagnetic fields.

Even mo...

The Politecnico di Milano has created the first integrated and fully tunable device based on spin waves, opening up new possibilities for the telecommunications of the future, far beyond current 5G and 6G standards. The study, published in the journal Advanced Materials, was conducted by a research group led by Riccardo Bertacco of the Department of Physics of the Politecnico di Milano, in collaboration with Philipp Pirro of Rheinland-Pfälzische Technische Universität and Silvia Tacchi of Istituto Officina dei Materiali—CNR-IOM.

Magnonics is an emerging technology that uses spin waves—collective excitations of electronic spins in magnetic materials—as an alternative to elect...

As drones survey forests, robots navigate warehouses and sensors monitor city streets, more of the world’s decision-making is occurring autonomously on the edge—on the small devices that gather information at the ends of much larger networks.

But making that shift to edge computing is harder than it seems. Although artificial intelligence (AI) models continue to grow larger and smarter, the hardware inside these devices remains tiny.

Engineers typically have two options, neither are ideal. Storing an entire AI model on the device requires significant memory, data movement and computing power that drains batteries. Offloading the model to the cloud avoids those hardware constraints, but the back-and-forth introduces lag, burns energy and presents security risks.

Researchers ...