JOURNAL ARTICLE

Reconfigurable memristor based on SrTiO3 thin-film for neuromorphic computing

Abstract

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain. However, the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail. In this work, Ag/SrTiO3(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip. By modulating the compliance current, two types of resistance switching, volatile and nonvolatile, can be obtained in amorphous STO thin film. This is attributed to the manipulation of the Ag conductive filament. Furthermore, through regulating electrical pulses and designing bionic circuits, the neuronal functions of leaky integrate and fire, as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation, are successfully realized. This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems.

Keywords:
Neuromorphic engineering Memristor Materials science Computer science Synapse Synaptic weight Nanotechnology Artificial neural network Voltage Electronic engineering Neuroscience Artificial intelligence Electrical engineering Engineering

Metrics

10
Cited By
1.66
FWCI (Field Weighted Citation Impact)
45
Refs
0.82
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Memory and Neural Computing
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Photoreceptor and optogenetics research
Life Sciences →  Neuroscience →  Cellular and Molecular Neuroscience
Neuroscience and Neural Engineering
Life Sciences →  Neuroscience →  Cellular and Molecular Neuroscience

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