In the current information age, the realization of memory devices with energy efficient
design, high storage density, nonvolatility, fast access, and low cost is still a great challenge …

Control of magnetization in magnetic nanostructures is essential for development of
spintronic devices because it governs fundamental device characteristics such as energy …

Inspired by the human brain, there is a strong effort to find alternative models of information
processing capable of imitating the high energy efficiency of neuromorphic information …

Spintronic nano-synapses and nano-neurons perform neural network operations with high
accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These …

Two-terminal memristors with internal Ca2+-like dynamics can be used to faithfully emulate
biological synaptic functions and have been intensively studied for neural network …

Recent progress in deep learning extends the capability of artificial intelligence to various
practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis …

Ferromagnets with binary states are limited for applications as artificial synapses for
neuromorphic computing. Here, it is shown how synaptic plasticity of a perpendicular …

Racetrack memory (RTM) is a novel spintronic memory-storage technology that has the
potential to overcome fundamental constraints of existing memory and storage devices. It is …

We demonstrate associative memory operations reminiscent of the brain using nonvolatile
spintronics devices. Antiferromagnet–ferromagnet bilayer-based Hall devices, which show …

Antiferromagnets offer a unique combination of properties including the radiation and
magnetic field hardness, the absence of stray magnetic fields, and the spin-dynamics …