Magnetic devices articles from across Nature Portfolio

Antiskyrmions, like skyrmions, are a form of topological spin texture, with a topological charge of opposite sign to the equivalent skyrmion with the same polarity. While antiskyrmions have been less explored, they offer some potential advantages for applications, and here, Guang et al demonstrate antiskyrmion motion within stripe domains.

A repeater takes an input signal and retransmits is, and a vital component for extending the range of signals. Here, Wang et al. demonstrate a magnon repeater, based on the bistable switching between low and high amplitude spin-waves.

Spin-polarized light-emitting diodes (spin-LEDs) convert the electronic spin information to photon circular polarization, but they are usually controlled only by external magnetic fields. Here, the authors report the realization of spin-LEDs based on 2D CrI₃/hBN/WSe₂ heterostructures, showing electrical tunability of the electroluminescence helicity.

Sensitive spin rectifier devices can be used to create rectennas that harvest ambient radiofrequency signals between –62 and –20 dBm, and can be used to create on-chip co-planar-waveguide-based spin rectifier arrays with large zero-bias sensitivity and high efficiency.

Designing a rectifier for harvesting low ambient radiofrequency energy and converting it into useful d.c. power is challenging. Now, a spin-rectifier-based rectenna and a spin-rectifier array with on-chip coplanar waveguides are designed for harvesting ambient radiofrequency signals with good sensitivity and efficiency.

Generating and controlling noncollinear spin textures is a promising route towards developing next-generation logic architectures beyond CMOS. Now, these spin textures can be engineered in twisted magnetic two-dimensional materials.

The combination of optical lithography with a transfer technique based on a sacrificial substrate enables the creation of a 3D-racetrack memory made from state-of-the-art spintronic materials.

An artificial spin ice, containing two types of nanomagnets, can perform reservoir computing with minor magnetic field loops and spin wave spectra acting as inputs and outputs, respectively.

In-memory computing chips based on magnetoresistive random-access memory devices can provide energy-efficient hardware for machine learning tasks.