journal article·2024-01-23Open access

A Purely Electronic Analog Memristive Device Based on a Squaraine Nanowire Mesh

Karl Griffin, Gareth Redmond

Abstract

High Resolution Image Download MS PowerPoint Slide Analog memristors offer the ability to gradually increment device conductance, akin to synaptic weight updating in the human brain, and a path toward emulation of brain-like functionality on a chip. Specifically, purely electronic resistive switching device operation may provide good device stability, fast device response, and microelectronics fabrication compatibility. In this context, nanowires (NWs) based on 2,4-bis[(4-diethylamino)-2-hydroxyphenyl]squaraine were prepared by non-solvent-induced precipitation and characterized in detail. Symmetric, unipolar (hole-only) metal–insulator–metal-type devices were formed by simple deposition of NW meshes on interdigitated gold electrodes. The DC I–V characteristics acquired from devices displayed pinched hysteretic I–V loops indicative of memristive behavior. Analysis of I–V data indicated Ohmic transport at low bias with carrier extraction facilitated by thermionic emission. At high bias, devices exhibited trap-limited space-charge-limited conduction in the presence of traps distributed in energy, which was enhanced by a Poole–Frenkel effect, with carrier extraction facilitated by Fowler–Nordheim tunneling. Overall, the data were consistent with purely electronic conduction. During voltage sweeps applied to NW mesh devices, I–V hysteresis was observed, consistent with modifications to current flow caused by carrier trapping/detrapping. On this basis, trap-enabled analog memristive device functionality was demonstrated in both rigid and flexible device formats by applying various voltage waveforms. Finally, the stable, repeated erase–write–read of multiple distinct conductance states over hundreds of voltage cycles during hours long periods of continuous use in an ambient atmosphere was demonstrated.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Neural dynamics and brain function

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Chemistry, University College Dublin, Belfield, Dublin 4, Ireland

Funding

Irish Research Council

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