Open Access

ARTICLE

Flexible Memristive Devices Based on Graphene Quantum-Dot Nanocomposites

Sung Won Hwang, Dae-Ki Hong^*

Department of System Semiconductor Engineering, SangMyung University, Cheonan-si, 31066, Korea

* Corresponding Author: Dae-Ki Hong. Email:

Computers, Materials & Continua 2022, 72(2), 3283-3297. https://doi.org/10.32604/cmc.2022.025931

Received 09 December 2021; Accepted 16 February 2022; Issue published 29 March 2022

Abstract

Artificial neural networks (ANNs) are attracting attention for their high performance in various fields, because increasing the network size improves its functioning. Since large-scale neural networks are difficult to implement on custom hardware, a two-dimensional (2D) structure is applied to an ANN in the form of a crossbar. We demonstrate a synapse crossbar device from recent research by applying a memristive system to neuromorphic chips. The system is designed using two-dimensional structures, graphene quantum dots (GQDs) and graphene oxide (GO). Raman spectrum analysis results indicate a D-band of 1421 cm⁻¹ that occurs in the disorder; band is expressed as an atomic characteristic of carbon in the sp² hybridized structure. There is also a G-band of 1518 cm⁻¹ that corresponds to the graphite structure. The G bands measured for RGO-GQDs present significant GQD edge-dependent shifts with position. To avoid an abruptly-formed conduction path, effect of barrier layer on graphene/ITO interface was investigated. We confirmed the variation in the nanostructure in the RGO-GQD layers by analyzing them using HR-TEM. After applying a negative bias to the electrode, a crystalline RGO-GQD region formed, which a conductive path. Especially, a synaptic array for a neuromorphic chip with GQDs applied was demonstrated using a crossbar array.

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Keywords

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