Related papers: Distinct memory properties in spin-wave reservoir …
The authors have numerically studied how to enhance reservoir computing performance by thoroughly extracting their spin-wave device potential for higher-dimensional information generation. The reservoir device has a 1-input exciter and…
Neuromorphic computing using spin waves is promising for high-speed nanoscale devices, but the realization of high performance has not yet been achieved. Here we show, using micromagnetic simulations and simplified theory with response…
Spin waves are collective perturbations in the orientation of the magnetic moments in magnetically ordered materials. Their rich phenomenology is intrinsically three-dimensional; however, the three-dimensional imaging of spin waves has so…
Strongly-interacting artificial spin systems are moving beyond mimicking naturally-occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically artificial spin systems…
Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness,…
The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning one of them being reservoir…
By performing numerical simulations for the handwritten digit recognition task, we demonstrate that a magnetic skyrmion lattice confined in a thin-plate magnet possesses high capability of reservoir computing. We obtain a high recognition…
Recently we demonstrated experimentally that microwave oscillators based on the time delay feedback provided by traveling spin waves could operate as reservoir computers. In the present paper, we extend this concept by adding the feature of…
Skyrmions have been proposed as new information carriers in racetrack memory devices. To realise such devices, a small size; high speed of propagation; and minimal skyrmion Hall angle are required. Synthetic antiferromagnets (SAFs) present…
We report on the experimental demonstration of an optical spin-wave memory, based on the atomic frequency comb (AFC) scheme, where the storage efficiency is strongly enhanced by an optical cavity. The cavity is of low finesse, but operated…
Synthetic antiferromagnetic layers (SAF) are incorporated into spin transfer nanopillars giving a layer composition [Co(bottom)/Ru/Co(fixed)]/Cu/Co(free), where square brackets indicate the SAF. The Co(bottom) and Co(fixed) layers are…
The Reservoir Computing (RC) paradigm posits that sufficiently complex physical systems can be used to massively simplify pattern recognition tasks and nonlinear signal prediction. This work demonstrates how random topological magnetic…
Speech recognition is a critical task in the field of artificial intelligence and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data…
A numerical analysis on the computational capability of physical reservoir computing utilizing a spin-torque oscillator with two free layers is reported. Conventional spintronics devices usually consist of two ferromagnets, where the…
Patterned magnetic nanostructures are advanced materials characterized by their unique magnetic properties at the nanoscale, which are the result of tailored geometric configurations and compositional engineering. As interest in…
Synthetic antiferromagnet (SAF) nanostructures with interfacial Dzyaloshinskii-Moriya interaction can host topologically distinct spin textures such as skyrmions and thus are regarded as promising candidates for both spintronics and…
Antiferromagnetic (AF) compounds possess distinct characteristics that render them promising candidates for advancing the application of spin degrees of freedom in computational devices. For instance, AF materials exhibit minimal…
Reservoir computers (RC) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of chaotic dynamics. Deep reservoir computers (deep-RC), in…
Resonant activation of a synthetic antiferromagnet (SAF) is known to result in a dynamic running state, where the SAF's symmetric spin-flop pair continuously rotates between the two antiparallel ground states of the system, with the two…
Reservoir computing (RC) is a state-of-the-art machine learning method that makes use of the power of dynamical systems (the reservoir) for real-time inference. When using biological complex systems as reservoir substrates, it serves as a…