Related papers: Shape-based Magnetic Domain Wall Drift for an Arti…

Racetrack memory based on magnetic domain walls (DWs) motion exhibits advantages of small volume and high reading speed. When compared to current-induced DW motion, voltage-induced DW motion exhibits lower dissipation. On the other hand,…

While spiking neural networks (SNNs) provide a biologically inspired and energy-efficient computational framework, their robustness and the dynamic advantages inherent to biological neurons remain significantly underutilized owing to…

Spiking neural network (SNN) is interesting due to its strong bio-plausibility and high energy efficiency. However, its performance is falling far behind conventional deep neural networks (DNNs). In this paper, considering a general class…

Spintronic devices are considered as promising candidates in implementing neuromorphic systems or hardware neural networks, which are expected to perform better than other existing computing systems for certain data classification and…

Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. Previous works have shown that converting Artificial Neural Networks (ANNs) into…

Within linear continuum theory, no magnetic texture can propagate faster than the maximum group velocity of its spin waves. Here we report a transient regime due to the appearance of additional antiferromagnetic textures that breaks the…

We propose a novel spin-orbit torque (SOT) driven and voltage-gated domain wall motion (DWM)-based MTJ device and its application in neuromorphic computing. We show that by utilizing the voltage-controlled gating effect on the DWM, the…

The growing demand for storage, due to big data applications, cannot be met by hard disk drives. Domain wall (DW) memory devices such as racetrack memory offer an alternative route to achieve high capacity storage. In DW memory, control of…

Spiking neural networks (SNN) are able to learn spatiotemporal features while using less energy, especially on neuromorphic hardware. The most widely used spiking neuron in deep learning is the Leaky Integrate and Fire (LIF) neuron. LIF…

The escalating energy demands of artificial intelligence pose a critical challenge to conventional computing. Leveraging the efficiency of event-driven, in-memory neuromorphic architectures into the superconducting circuits with ultra-high…

In order to ease the analysis of error propagation in neuromorphic computing and to get a better understanding of spiking neural networks (SNN), we address the problem of mathematical analysis of SNNs as endomorphisms that map spike trains…

We show that spin transfer torque from direct spin-polarized current applied parallel to a magnetic domain wall (DW) induces DW motion in a direction independent of the current polarity. This unidirectional response of the DW to spin torque…

Conventional magnetic domain walls are characterized by reorientation of local spins. However, what occurs at the boundary of itinerant magnets is largely unknown. Here using spin-sensitive scanning tunneling microscopy, we investigated the…

Ultrafast optical control of magnetic textures offers new opportunities for energy-efficient, high-speed spintronic devices. While uniform magnetization reversal via all-optical switching is well established, the formation dynamics of…

Domain walls are the transition regions between two magnetic domains. These objects have been very relevant during the last decade, not only due to their intrinsic interest in the development of novel spintronics devices but also because of…

Travelling waves of neural firing activity are observed in brain tissue as a part of various sensory, motor and cognitive processes. They represent an object of major interest in the study of excitable networks, with analysis conducted in…

Finding a new control parameter for magnetic domain wall (DW) motion in magnetic nanostructures is important in general and in particular for the spintronics applications. Here, we show that a circularly polarized magnetic field (CPMF) at…

Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial…

Antiferromagnetic spintronics is an emerging research field which aims to utilize antiferromagnets as core elements in spintronic devices. A central motivation toward this direction is that antiferromagnetic spin dynamics is expected to be…

Research on current-induced domain wall (DW) motion in heavy metal/ferromagnet structures is crucial for advancing memory, logic, and computing devices. Here, we demonstrate that adjusting the angle between the DW conduit and the current…