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Threshold Switching in Single Metal‐Oxide Nanobelt Devices Emulating an Artificial Nociceptor

Ming XiaoCentre for Advanced Materials Joining University of Waterloo Waterloo Ontario N2L 3G1 CanadaDaozhi ShenCentre for Advanced Materials Joining University of Waterloo Waterloo Ontario N2L 3G1 CanadaMoritz H. FutscherCenter for Nanophotonics AMOLF Science Park 104 1098 XG Amsterdam the NetherlandsBruno EhrlerCenter for Nanophotonics AMOLF Science Park 104 1098 XG Amsterdam the NetherlandsKevin P. MusselmanDepartment of Mechanics and Mechatronics Engineering University of Waterloo Waterloo Ontario N2L 3G1 CanadaW. W. DuleyCentre for Advanced Materials Joining University of Waterloo Waterloo Ontario N2L 3G1 CanadaY. ZhouCentre for Advanced Materials Joining University of Waterloo Waterloo Ontario N2L 3G1 Canada
2019en
ABI

Аннотация

Abstract Electronic devices that can simulate the dynamics of neurotransmission in the human body are of great interest for the development of artificial intelligence in modern information technology. An artificial nociceptor realized by a single metal‐oxide nanobelt device with a unique capacitive‐coupled threshold switching behavior is demonstrated. Via thermal admittance spectroscopy and temperature‐dependent sweeping study, the properties of the nanobelt devices are determined by Schottky emission at low bias and by defect‐assisted quantum tunneling at high bias subject to a threshold voltage. The low activation energy associated with dynamic electron trapping gives rise to a voltage‐dependent volatile threshold switching behavior. This threshold switching behavior allows the emulation of several characteristic features of a nociceptor, a critical type of sensory neuron in the human body, including “threshold,” “relaxation,” “no adaptation,” “allodynia,” and “hyperalgesia” behaviors, essential for the realization of electronic sensory receptors that detect noxious stimuli and signal rapid warning to the central nervous system. One‐dimensional metal oxide nanobelt devices of this type yield multifunctional nociceptor performance that is fundamental for applications in artificial intelligence systems, representing a key step in the realization of neural integrated devices via a bottom‐up approach.

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