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Electroformed, Self‐Connected Tin Oxide Nanoparticle Networks for Electronic Reservoir Computing
Author(s) -
Le Phuong Y.,
Murdoch Billy J.,
Barlow Anders J.,
Holland Anthony S.,
McCulloch Dougal G.,
McConville Chris F.,
Partridge Jim G.
Publication year - 2020
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.202000081
Subject(s) - electroforming , materials science , electrode , nanoparticle , non blocking i/o , optoelectronics , tin , tin oxide , oxide , nanotechnology , thin film , joule (programming language) , memristor , electronic engineering , electrical engineering , metallurgy , layer (electronics) , engineering , biochemistry , chemistry , catalysis , efficient energy use
Interconnected SnO x nanoparticle (NP) networks are electroformed within a semi‐insulating SnO x thin‐film and between lateral electrodes. During this top‐down process, Joule heating, disproportionation, and de‐wetting of the SnO x thin‐film precede the formation of the NP networks. The same lateral electrodes used for electroforming are used to probe the network and reveal its complex electrical characteristics. Higher‐order harmonic generation is observed and the internal short‐term memory effects of the NP networks enable temporal inputs to be mapped into reservoir states for subsequent linear readout without training. Reservoir computing functionality is demonstrated with no requirement for high‐vacuum or cryo‐cooled environments.