相关论文: Real Space Imaging of Field-Driven Decision-Making…
Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled…
Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-effcient cognitive intelligence. The computational model attempt to exploit the intrinsic device…
Precise control of magnetic domain formation at the nanoscale remains constrained by stochastic defect-mediated and unstable pinning, limiting scalability and reproducibility in spintronic architectures. Here we demonstrate that spatially…
As artificial intelligence (AI) advances into diverse applications, ensuring reliability of AI models is increasingly critical. Conventional neural networks offer strong predictive capabilities but produce deterministic outputs without…
Micromagnetic instabilities and non-uniform magnetization states play a significant role in spin transfer induced switching of nanometer scale magnetic elements. Here we model domain wall mediated switching dynamics in perpendicularly…
Nanoscale magnetic junction provides a useful approach to act as the building block for magnetoresistive random access memories (MRAM), where one of the key issues is to control the magnetic domain configuration. Here, we study the domain…
Magnetic tunnel junctions operating in the superparamagnetic regime are promising devices in the field of probabilistic computing, which is suitable for applications like high-dimensional optimization or sampling problems. Further, random…
For viable applications, spintronic devices based e.g. on domain wall motion need to be highly reliable with stable magnetization states and highly reproducible switching pathways transforming one state to another. The existence of multiple…
The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring…
We investigate the impact of line-edge roughness on current-driven domain wall dynamics in ferromagnetic racetracks. Modeling the edge disorder as a spatially correlated Ornstein-Uhlenbeck process, we demonstrate that even minimal…
Thermodynamic-driven filament formation in redox-based resistive memory and the impact of thermal fluctuations on switching probability of emerging magnetic switches are probabilistic phenomena in nature, and thus, processes of binary…
We show that nano-scale variations of the order parameter in strongly-correlated systems can induce local spatial regions such as domain walls that exhibit electronic properties representative of a different, but nearby, part of the phase…
The motions of domain walls driven by magnetic field in soft magnetic nanostripes were calculated. The domain walls reveal steady motions in the low fields and oscillations of their internal structure above a critical field. A developed…
We report on the controllable pinning of domain walls in stripes with perpendicular magnetic anisotropy by magnetostatic coupling to magnetic vortices in disks located above the stripe. Pinning mechanisms and depinning fields are reported.…
The impressive performance of artificial neural networks has come at the cost of high energy usage and CO$_2$ emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative…
Controlling magnetic textures at ever smaller length and time scales is of key fundamental and technological interest. Achieving nanoscale control often relies on finding an external stimulus that is able to act on that small length scales,…
Domain wall memories have undergone several changes over the years for faster shift, read, and write operations; however, fundamental issues persist due to creating pinning sites topographically along the nanowire. The deformity in notches…
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…
One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities…
In this work we perform investigations of the competition between domain-wall pinning and attraction by anti-notches and finite device borders. The conditions for optimal geometries, which can attain a stable domain-wall pinning, are…