Memory

Researchers from Korea have designed a new MRAM structure, based on graphene, that offers higher efficiency (and lower heat generation) compared to existing MRAM solutions. The design of the MRAM device is based on a graphene layer sandwiched between a magnetic insulator (yttrium iron garnet) and a ferroelectric material (PVDF-TrFE). Upon application of a voltage pulse, the current flow through the graphene is altered, enabling the storage of binary data based on this current direction.

The recent study demonstrates non-volatile control of spin-charge conversion at room temperature in graphene-based heterostructures through Fermi level tuning. The team used a polymeric ferroelectric film to induce non-volatile charging in graphene. To demonstrate the switching of spin-to-charge conversion, the scientists performed ferromagnetic resonance and inverse Edelstein effect experiments. 

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.

TDK Corporation has announced the development of a neuromorphic element called a “spin-memristor” that has very low power consumption. By mimicking the energy-efficient operation of the human brain, this element could cut the power consumption of AI applications down to 1/100th of traditional devices. Collaborating with the French research organization CEA (Alternative Energies and Atomic Energy Commission), TDK has shown that its “spin-memristor” can serve as the basic element of a neuromorphic device. 

Going forward, TDK will collaborate with the Center for Innovative Integrated Electronic Systems at Tohoku University on the practical development of the technology.

Researchers from the University of Nebraska-Lincoln and University of Latvia have announced "a breakthrough in antiferromagnetic spintronics" that could expand the nanotechnology’s capabilities, which have been limited by their need for excessive power. 

The team showed that introducing boron — a process called B-doping — into magnetoelectric oxides can control magnetic fields at the high temperatures prevalent in electronics. This long has been the “holy grail” of such research, said Christian Binek, Charles Bessey Professor of physics.

Researchers from Tohoku University and Toho University have demonstrated chirality switching by electric current pulses at room temperature in a thin-film MnAu₂ helimagnetic conductor. The team also succeeded in detecting the chirality at zero magnetic fields by means of simple transverse resistance measurement utilizing the spin Berry phase in a bilayer device composed of MnAu₂ and a spin Hall material Pt. These results may pave the way to helimagnet-based spintronics. 

Helimagnetic structures, in which the magnetic moments are spirally ordered, host an internal degree of freedom called chirality corresponding to the handedness of the helix. The chirality seems quite robust against disturbances and is therefore promising for next-generation magnetic memory. While the chirality control was recently achieved by the magnetic field sweep with the application of an electric current at low temperature in a conducting helimagnet, problems such as low working temperature and cumbersome control and detection methods have to be solved in practical applications.

Researchers at North Carolina State University, University of Illinois at Urbana-Champaign, Duke University and Sivananthan Laboratories have relied on a printable organic polymer, that assembles into chiral structures when printed, to reliably measure the amount of charge produced in spin-to-charge conversion within a spintronic material at room temperature. 

The polymer’s tunable qualities and versatility make it desirable not only for less expensive, environmentally friendly, printable electronic applications, but also for use in understanding chirality and spin interactions more generally.

The EU project 2DSPIN-TECH aims to pave the way for significantly faster and more energy-efficient computer memories. Last week, the project kickoff event took place, with seven partners and €4 million in funding. The project spans three years and is conducted within the framework of the EU’s Graphene Flagship, a multibillion-dollar initiative launched over a decade ago to stimulate research and innovation in graphene and other two-dimensional materials.

“Our ambition is to create novel spintronic memory devices based on two-dimensional quantum materials, significantly reducing energy consumption, promoting sustainability, and enhancing the overall performance of computer memories. This is crucial for the future of information technology,” says Saroj Dash, Professor of quantum component physics at Chalmers University of Technology and coordinator of 2DSPIN-TECH.

An interdisciplinary collaboration of researchers from South Korea and Singapore recently reported a significant advance towards achieving spin-polarized van der Waals heterostructures. The team designed a spin-selective memtransistor device using single-layer graphene deposited on the antiferromagnetic van der Waals magnetic insulator CrI₃. 

Transport measurements combined with first-principles calculations provide unprecedented insights into tailoring reciprocal magnetic proximity interactions to generate and probe proximitized magnetism in graphene at room temperature.

Researchers at the National Synchrotron Light Source II at Brookhaven National Laboratory, University of California Davis, University of Colorado Springs, Stockholm University, National Institute of Standards and Technology, University of California San Diego, Ca’ Foscari University of Venice, and Elettra Sincrotrone Trieste have conducted an experiment with magnetic materials and ultrafast lasers that could advance energy-efficient data storage.

"We wanted to study the physics of light-magnet interaction," said Rahul Jangid, who led the data analysis for the project while earning his Ph.D. in materials science and engineering at UC Davis under associate professor Roopali Kukreja. "What happens when you hit a magnetic domain with very short pulses of laser light?"

Scientists at the University of Wisconsin–Madison, University of California, Cornell University, University of Nebraska, Arizona State University and Tsinghua University have found a unique property of the material Ba(Pb,Bi)O₃: it exhibits extremely high spin orbit torque, a property useful in the field of spintronics. The materials was previously found to act as a rare type of superconductor that could operate at higher temperatures. 

The combination of these two properties makes this and similar materials potentially important in developing the next generation of fast, efficient memory and computing devices.