Tunable Stochasticity in an Artificial Spin Network
| Autor: | Daniel Lacour, Vojtěch Schánilec, Nicolas Rougemaille, Michel Hehn, Nicolas Reyren, Maryam Massouras, Benjamin Canals, Dédalo Sanz-Hernández, François Montaigne, Julie Grollier, Damien Querlioz, Karim Bouzehouane |
|---|---|
| Přispěvatelé: | Unité mixte de physique CNRS/Thales (UMPhy CNRS/THALES), THALES-Centre National de la Recherche Scientifique (CNRS), Institut Jean Lamour (IJL), Université de Lorraine (UL)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Micro et NanoMagnétisme (MNM), Institut Néel (NEEL), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Théorie de la Matière Condensée (TMC), Centre de Nanosciences et de Nanotechnologies (C2N), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Materials science
FOS: Physical sciences 02 engineering and technology neuromorphic 010402 general chemistry Topology 01 natural sciences computing Mesoscale and Nanoscale Physics (cond-mat.mes-hall) General Materials Science stochastic [PHYS.COND]Physics [physics]/Condensed Matter [cond-mat] Randomness [PHYS]Physics [physics] Condensed Matter - Mesoscale and Nanoscale Physics Artificial neural network domain-wall Mechanical Engineering nanomagnetic Metamaterial 021001 nanoscience & nanotechnology 0104 chemical sciences Magnetic field Lattice (module) Galton board Domain wall (magnetism) Neuromorphic engineering Mechanics of Materials Spin network 0210 nano-technology |
| Zdroj: | Advanced Materials Advanced Materials, Wiley-VCH Verlag, 2021, 33 (17), pp.2008135. ⟨10.1002/adma.202008135⟩ |
| ISSN: | 0935-9648 1521-4095 |
| DOI: | 10.1002/adma.202008135⟩ |
| Popis: | Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. This type of tunable stochastic network opens new paths towards post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks. Comment: 24 pages, 10 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text