Related papers: Ultra-low Power Domain Wall Device for Spin-based …
Neuromorphic computing promises brain-like efficiency, yet today's multi-chip systems scale over PCBs and incur orders-of-magnitude penalties in bandwidth, latency, and energy, undermining biological algorithms and system efficiency. We…
Neuro-inspired computing architectures are one of the leading candidates to solve complex, large-scale associative learning problems. The two key building blocks for neuromorphic computing are the synapse and the neuron, which form the…
Discovering alternative ways to drive domain wall (DW) dynamics is crucial for advancing spintronic applications. Here we demonstrate via atomistic spin dynamics simulations that optical torques can efficiently drive 90^{\circ} DWs in the…
Most of the existing researches on the dynamics of a domain wall (DW) have focused on the effect of DC biases, where the induced velocity is determined by the bias strength. Here we show that AC biases such as a field or a current are also…
Spin-wave technology (magnonics) has the potential to further reduce the size and energy consumption of information processing devices. In the submicrometer regime (exchange spin waves), topological defects such as domain walls may…
The ultrafast control of nanoscale spin textures such as magnetic domain walls or skyrmions is essential for advancing high-speed, high-density spintronics. However, imaging their dynamics will require a technique that combines nanometer…
Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of…
In recent years, new methods for solving partial differential equations (PDEs) such as Monte Carlo random walk methods have gained considerable attention. However, due to the lack of hardware-intrinsic randomness in the conventional von…
Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Previous proposals for spintronic artificial neurons…
We present an analytical calculation of the velocity of a single 180 degree domain wall in a magnetic structure with reduced thickness and/or lateral dimension under the combined action of an external applied magnetic field and an…
Spin waves are perturbations in the relative orientation of magnetic moments in a continuous magnetic system, which have been proposed as a new kind of information carrier for spin-based low power applications. For this purpose, a major…
Antiferromagnets (AFMs) have great benefits for spintronic applications such as high frequencies (up to THz), high speeds (up to tens of km/s) of magnetic excitations, and field-free operation. Advanced devices will require high-speed…
Recent studies have revealed that domain walls in magnetic nanostructures can serve as compact, energy-efficient spin-wave waveguides for building magnonic devices that are considered promising candidates for overcoming the challenges and…
Magnetic domain-walls (DWs) with a preferred chirality exhibit very efficient current-driven motion. Since structural inversion asymmetry (SIA) is required for their stability, the observation of chiral domain walls in highly symmetric…
In magnetic nanowires with perpendicular magnetic anisotropy (PMA) magnetic domain walls (DW) are narrow and can move rapidly driven by current induced torques. This enables important applications like high-density memories for which the…
We present a theoretical study of the scattering of spin waves by a domain wall (DW) in a ferrimagnetic (FiM) spin chain in which two sublattices carry spins of unequal magnitudes. We find that a narrow, but atomically smooth FiM DW…
We present time-resolved measurements of the displacement of magnetic domain-walls (DWs) driven by vertical spin-polarized currents in track-shaped magnetic tunnel junctions. In these structures we observe very high DW velocities (600 m/s)…
Manufacturing-viable neuromorphic chips require novel computer architectures to achieve the massively parallel and efficient information processing the brain supports so effortlessly. Emerging event-based architectures are making this dream…
The memory demands of large-scale deep neural networks (DNNs) require synaptic weight values to be stored and updated in off-chip memory like dynamic random-access memory, which reduces energy efficiency and increases training time.…
Spin Hall effect converts charge current to spin current, which can exert spin-torque to switch the magnetization of a nanomagnet. Recently, it is shown that the ratio of spin current to charge current using spin Hall effect can be made…