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A ferrofluid-based neural network: design of an analogue associative memory
KTH, School of Engineering Sciences (SCI), Applied Physics, Nanostructure Physics.
KTH, School of Engineering Sciences (SCI), Applied Physics, Nanostructure Physics.ORCID iD: 0000-0003-2339-1692
2009 (English)In: New Journal of Physics, ISSN 1367-2630, E-ISSN 1367-2630, Vol. 11Article in journal (Refereed) Published
Abstract [en]

We analyse an associative memory based on a ferrofluid, consisting of a system of magnetic nano-particles suspended in a carrier fluid of variable viscosity subject to patterns of magnetic fields from an array of input and output magnetic pads. The association relies on forming patterns in the ferrofluid during a training phase, in which the magnetic dipoles are free to move and rotate to minimize the total energy of the system. Once equilibrated in energy for a given input-output magnetic field pattern pair, the particles are fully or partially immobilized by cooling the carrier liquid. Thus produced particle distributions control the memory states, which are read out magnetically using spin-valve sensors incorporated into the output pads. The actual memory consists of spin distributions that are dynamic in nature, realized only in response to the input patterns that the system has been trained for. Two training algorithms for storing multiple patterns are investigated. Using Monte Carlo simulations of the physical system, we demonstrate that the device is capable of storing and recalling two sets of images, each with an accuracy approaching 100%.

Place, publisher, year, edition, pages
2009. Vol. 11
Keyword [en]
monte-carlo simulations, chain-like clusters, ferromagnetic particles, dispersions, field
Identifiers
URN: urn:nbn:se:kth:diva-18150DOI: 10.1088/1367-2630/11/2/023003ISI: 000263041100003Scopus ID: 2-s2.0-62649150220OAI: oai:DiVA.org:kth-18150DiVA, id: diva2:336196
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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Korenivski, Vladislav

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CiteExportLink to record
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  • apa
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  • de-DE
  • en-GB
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  • nn-NB
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  • Other locale
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Output format
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