November 2024

A team of researchers from CIC nanoGUNE, IKERBASQUE, IMEC and CNRS have reported a spintronic device that leverages proximity effects alone, specifically a 2D graphene-based spin valve. The functioning of this valve relies only on the proximity to the van der Waals magnet Cr₂Ge₂Te₆. Spin precession measurements showed that the graphene acquires both spin–orbit coupling and magnetic exchange coupling when interfaced with the Cr₂Ge₂Te₆. This leads to spin generation by both electrical spin injection and the spin Hall effect, while retaining spin transport. The simultaneous presence of spin–orbit coupling and magnetic exchange coupling also leads to a sizeable anomalous Hall effect.

The primary objective of this recent study was to tackle a long-standing research challenge, namely that of realizing the first-ever seamless 2D spintronic device. The spin valve they developed could enable the manipulation and transport of spin entirely in the 2D plane.

Researchers from the Tokyo University of Science have proposed a novel magnetic RAM-based architecture that leverages spintronics to realize smaller, more efficient AI-capable circuits.

(a) Structure of the proposed neural network, which uses three-valued gradients during backpropagation (training) rather than real numbers, thus minimizing computational complexity. (b) A novel magnetic RAM cell leveraging spintronics for implementing the proposed technique in a computing-in-memory architecture.   

Artificial intelligence (AI) and the Internet of Things (IoT) are two technological fields that have been developing at an increasingly fast pace over the past decade. By excelling at tasks such as data analysis, image recognition, and natural language processing, AI systems have become undeniably powerful tools in both academic and industry settings. Meanwhile, miniaturization and advances in electronics have made it possible to massively reduce the size of functional devices capable of connecting to the Internet. Engineers and researchers alike foresee a world where IoT devices are ubiquitous, comprising the foundation of a highly interconnected world. However, bringing AI capabilities to IoT edge devices presents a significant challenge. Artificial neural networks (ANNs)—one of the most important AI technologies—require substantial computational resources. Meanwhile, IoT edge devices are inherently small, with limited power, processing speed, and circuit space. Developing ANNs that can efficiently learn, deploy, and operate on edge devices is a major hurdle.

Quantum computers based on spin photons and diamond promise significant advantages over competing quantum computing technologies, such as lower cooling requirements, longer operating times and lower error rates. The consortium of the BMBF project SPINNING coordinated by Fraunhofer IAF has succeeded in advancing the development of spin-photon-based quantum computers. The partners recently presented the interim project results at the mid-term meeting of the BMBF funding measure Quantum Computer Demonstration Setups in Berlin.

There are still several competing approaches to realizing quantum computers, each with specific advantages and disadvantages in terms of hardware and software, ranging from reliability and energy consumption to compatibility with conventional systems. Under the coordination of the Fraunhofer Institute for Applied Solid State Physics IAF, a consortium of 28 partners is working on the project "SPINNING - Diamond spin-photon-based quantum computer" to develop a quantum computer based on spin photons and diamond. This should be characterized by lower cooling requirements, longer operating times and lower error rates than other quantum computing approaches. The hybrid concept of the spin-photon-based quantum computer also provides for greater scalability and connectivity, which enables flexible connection with conventional computers.