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Dynamically Driven Emergence in a Nanomagnetic System
Author(s) -
Dawidek Richard W.,
Hayward Thomas J.,
Vidamour Ian T.,
Broomhall Thomas J.,
Venkat Guru,
Mamoori Mohanad Al,
Mullen Aidan,
Kyle Stephan J.,
Fry Paul W.,
Steinke NinaJuliane,
Cooper Joshaniel F. K.,
Maccherozzi Francesco,
Dhesi Sarnjeet S.,
Aballe Lucia,
Foerster Michael,
Prat Jordi,
Vasilaki Eleni,
Ellis Matthew O. A.,
Allwood Dan A.
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202008389
Subject(s) - neuromorphic engineering , scalability , materials science , ring (chemistry) , population , magnetic field , biological system , fabrication , field (mathematics) , nanotechnology , computer science , physics , artificial intelligence , artificial neural network , biology , mathematics , medicine , chemistry , database , demography , alternative medicine , organic chemistry , pathology , quantum mechanics , sociology , pure mathematics
Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring‐shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field‐dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non‐linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers.