Memory

The University of California, Riverside, according to reports, has been awarded nearly $4 million through the UC National Laboratory Fees Research Program to lead a major research initiative in antiferromagnetic spintronics. Over the next three years, the project will explore how antiferromagnetic materials can be used to push the boundaries of modern microelectronics.

“The semiconductor microelectronics industry is looking for new materials, new phenomena, and new mechanisms to sustain technological advances,” said Jing Shi, a distinguished professor of physics and astronomy at UCR and the award’s principal investigator. “With co-principal investigators at UC San Diego, UC Davis, UCLA, and Lawrence Livermore National Laboratory, we aim to cement the University of California’s leadership in this area and obtain extramural center and group funding in the near future.”

The data storage capacity of multi-terabyte hard drives is several million megabytes, but their data transfer rates are only a few hundred megabytes per second, due to their reliance on tiny magnetic structures. The development of memory devices that operate at picosecond timescales could speed data transfer and improve access to digital information. However, ultrafast control of magnetization states in magnetically ordered systems, like hard drives, is a challenge.

Researchers from Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and TU Dortmund University have attempted to remove speed restrictions in hard drives, by using short current pulses and spintronic effects. Instead of electrical pulses, the team used ultrashort terahertz (THz) light pulses to enable the readout of magnetic structures in just picoseconds.

UC Riverside has received a Collaborative Research and Training Award of nearly $4 million from the UC National Laboratory Fees Research Program to explore how antiferromagnetic spintronics can be used to advantage in advanced memory and computing. The three-year project aims to advance microelectronics using antiferromagnetic materials, an ultrafast spin-based technology.

“The semiconductor microelectronics industry is looking for new materials, new phenomena, and new mechanisms to sustain technological advances,” said Jing Shi, a distinguished professor of physics and astronomy at UCR and the award’s principal investigator. “With co-principal investigators at UC San Diego, UC Davis, UCLA, and Lawrence Livermore National Laboratory, we aim to cement the University of California’s leadership in this area and obtain extramural center and group funding in the near future.”

Researchers from ICN2, ICMAB-CSIC and the Bulgarian Academy of Science have shown how the interaction with palladium diselenide (PdSe₂) can modify and enhance graphene’s spintronic performance. The team's finding improve existing understanding of spin dynamics in graphene-based van der Waals heterostructures and could be key for developing more efficient computing devices.

Van der Waals heterostructures are materials formed by combining layers of different ultra-thin materials stacked on top of each other. In recent years, these structures have proven to be very useful for studying and understanding unusual physical phenomena, making them promising candidates for the development of new technologies. The new study analyzed the interactions that occur in a graphene and palladium diselenide (PdSe₂) heterostructure. The team stresses: "Our results showed that PdSe₂ can induce significant changes in the spin transport properties and dynamics of graphene, providing new possibilities for controlling information-carrying spin currents”. These findings constitute an important step forward in elucidating spin physics in van der Waals heterostructures and could allow for spin-logic devices in the future.

Tohoku University researchers have observed an opto-magnetic torque approximately five times more efficient than in conventional magnets. This breakthrough could be extremely beneficial for the development of light-based spin memory and storage technologies.

Opto-magnetic torque is a method which can generate force on magnets, which can be used to change the direction of magnets by light more efficiently. By creating alloy nanofilms with up to 70% platinum dissolved in cobalt, the team discovered that the unique relativistic quantum mechanical effects of platinum significantly boost the magnetic torque.

Researchers at the University of Utah and the University of California, Irvine (UCI), have set out to better understand a property known as spin-torque, that is crucial for the electrical manipulation of magnetization that’s required for the next generations of storage and processing technologies. 

The spintronic prototype device that exploits the anomalous Hall torque effect. Image from: University of Utah

The scientists have discovered a new type of spin–orbit torque, in a recent study that demonstrated a new way to manipulate spin and magnetization through electrical currents, a phenomenon that they’ve dubbed the anomalous Hall torque.

Researchers at the Research Center for Materials Nanoarchitectonics (MANA) recently proposed a method to create ferroelectric-ferromagnetic materials, opening doors to advancing spintronics and memory devices.

In 1831, Michael Faraday discovered the fundamental connection between electricity and magnetism, demonstrating that a changing magnetic field induces electric current in a conductor. In a recent study, MANA researchers proposed a method for designing ferroelectric-ferromagnetic (FE-FM) materials, which exhibit both ferroelectric and ferromagnetic properties, enabling the manipulation of magnetic properties using electric fields and vice versa. Such materials are highly promising for spintronics and memory devices. The advantage of FE-FM materials, extremely rare in nature, is their ability to achieve the cross-control by relatively low electric and magnetic fields. The study, led by Principal Researcher Igor Solovyev from MANA, NIMS, included contributions from Dr. Ryota Ono from MANA, NIMS, and Dr. Sergey Nikolaev from the University of Osaka, Japan.

A team of Tohoku University researchers recently investigated a cobalt-manganese-iron alloy thin film that demonstrates a high perpendicular magnetic anisotropy (PMA) - key aspects for fabricating MRAM devices using spintronics.

"This is the first time a cobalt-manganese-iron alloy has strongly shown large PMA," says Professor Shigemi Mizukami from Tohoku University, "We previously discovered this alloy showed a high tunnel magnetoresistance (TMR) effect, but it is rare that an alloy potentially shows both together." For example, Iron-cobalt-boron alloys, which are conventionally used for MRAM, possess both traits, but their PMA is not strong enough.

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.