Tunable Stochasticity in an Artificial Spin Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APU142119" target="_blank" >RIV/00216305:26620/21:PU142119 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1002/adma.202008135" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/adma.202008135</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/adma.202008135" target="_blank" >10.1002/adma.202008135</a>
Alternative languages
Result language
angličtina
Original language name
Tunable Stochasticity in an Artificial Spin Network
Original language description
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 toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10302 - Condensed matter physics (including formerly solid state physics, supercond.)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ADVANCED MATERIALS
ISSN
0935-9648
e-ISSN
1521-4095
Volume of the periodical
33
Issue of the periodical within the volume
17
Country of publishing house
DE - GERMANY
Number of pages
7
Pages from-to
„2008135-1“-„2008135-7“
UT code for WoS article
000630218000001
EID of the result in the Scopus database
2-s2.0-85103011275