Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged under the name of physical reservoir computing. In this paradigm, an input-driven dynamical system (the reservoir) is exploited and trained to perform computational tasks. Recent spintronic thin-film reservoirs show state-of-the-art performances despite simplicity in their design. Here, we explore film geometry and show that simple changes to film shape and input location can lead to greater memory and improved performance across various time-series tasks.
Original languageEnglish
Title of host publicationUCNC 2021: Unconventional Computation and Natural Computation
PublisherSpringer
Pages19
Number of pages34
Volume12984
ISBN (Electronic)978-3-030-87993-8
ISBN (Print)978-3-030-87992-1
DOIs
Publication statusPublished - 11 Oct 2021
EventUnconventional Computation and Natural Computation 2021
19th International Conference, UCNC 2021, Espoo, Finland, October 18–22, 2021
- Espoo, Finland, Espoo, Finland
Duration: 18 Oct 202122 Oct 2021
Conference number: 19

Publication series

NameLNTCS
PublisherSpringer
Number1
Volume12984

Conference

ConferenceUnconventional Computation and Natural Computation 2021
19th International Conference, UCNC 2021, Espoo, Finland, October 18–22, 2021
Abbreviated titleUCNC 2021
Country/TerritoryFinland
CityEspoo
Period18/10/2122/10/21

Keywords

  • magnetic thin films
  • magnetic materials
  • unconventional computing
  • reservoir computing
  • in-materio computing
  • substrate geometry

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