Micromagnetic Simulations of Chaotic Ferromagnetic Nanofiber Networks

Investigation of the Morphological Structure of Needle-Free Electrospun Magnetic Nanofiber Mats

Electrospun magnetic nanofibers are promising for a variety of applications in biomedicine, energy storage, filtration or spintronics. The surface morphology of nanofiber mats plays an important role for defined application areas. In addition, the distribution of magnetic particles in nanofibers exerts an influence on the final properties of nanofiber mats. A simple method for the production of magnetic nanofiber mats by the addition of magnetic nanoparticles in an electrospinning polymer solution was used in this study. In this work, magnetic nanofibers (MNFs) were prepared by needle-free electrospinning technique from poly(acrylonitrile) (PAN) in the low-toxic solvent dimethylsulfoxide (DMSO) and 20 wt% Fe3O4 at different parameter conditions such as PAN concentration, voltage and ultrasonic bath. The distribution of nanoparticles in the fiber matrix was investigated as well as the chemical and morphological properties of the resulting magnetic nanofibers. In addition, the surface morphology of magnetic nanofiber mats was studied by confocal laser scanning microscope (CLSM), scanning electron microscope (SEM), Fourier transform infrared microscope (FTIR) and ImageJ software, and distribution of Fe3O4 particles in the matrix was investigated by energy dispersive X-ray spectroscopy (EDX).

Stochastic vs. deterministic magnetic coding in designed cylindrical nanowires for 3D magnetic networks

Advances in cylindrical nanowires for 3D information technologies profit from intrinsic curvature that introduces significant differences with regards to planar systems. A model is proposed to control the stochastic and deterministic coding of remanent 3D complex vortex configurations in designed multilayered (magnetic/non-magnetic) cylindrical nanowires. This concept, introduced by micromagnetic simulations, is experimentally confirmed by magnetic imaging in FeCo/Cu multilayered nanowires. The control over the random/deterministic vortex states configurations is achieved by a suitable geometrical interface tilting of almost non-interacting FeCo segments with respect to the nanowire axis, together with the relative orientation of the perpendicular magnetic field. The proper design of the segments' geometry (e.g. tilting) in cylindrical nanowires opens multiple opportunities for advanced nanotechnologies in 3D magnetic networks.

Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers

Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coupled semicircle fibers in which domain walls are excited by rotating magnetic fields (inputs), leading to different output signals that can be used for stochastic data processing, mimicking biological synaptic activity and thus being suitable as artificial synapses in artificial neural networks.

Magnetic Elements for Neuromorphic Computing

Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems.