This paper reviews the state of the art in spin-torque and spin-Hall-effect-driven nano-
oscillators. After a brief introduction to the underlying physics, the authors discuss different …

This article reviews static and dynamic interfacial effects in magnetism, focusing on
interfacially driven magnetic effects and phenomena associated with spin-orbit coupling and …

In recent years, artificial neural networks have become the flagship algorithm of artificial
intelligence. In these systems, neuron activation functions are static, and computing is …

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

We investigated physical reservoir computing (RC) using a vortex-type spin torque oscillator
(STO) as a resource of nonlinear dynamics, which is essential for processing information in …

Spintronic nanodevices have ultrafast nonlinear dynamic and recurrence behaviors on a
nanosecond scale that promises to enable a high-performance spintronic reservoir …

The reservoir computing neural network architecture is widely used to test hardware systems
for neuromorphic computing. One of the preferred tasks for bench-marking such devices is …

The concept of spin-torque-driven high-frequency magnetization dynamics, allows the
potential construction of complex networks of non-linear dynamical nanoscale systems …

Physical reservoir computing is a framework that enables energy‐efficient information
processing by using physical systems. Nonlinear dynamics in physical systems provide a …

Mutual synchronization of N serially connected spintronic nano-oscillators boosts their
coherence by N and peak power by N 2. Increasing the number of synchronized nano …