@Article{cmc.2022.025931,
AUTHOR = {Sung Won Hwang, Dae-Ki Hong},
TITLE = {Flexible Memristive Devices Based on Graphene Quantum-Dot Nanocomposites},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {72},
YEAR = {2022},
NUMBER = {2},
PAGES = {3283--3297},
URL = {http://www.techscience.com/cmc/v72n2/47214},
ISSN = {1546-2226},
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<sup>−1</sup> that occurs in the disorder; band is expressed as an atomic characteristic of carbon in the sp<sup>2</sup> hybridized structure. There is also a G-band of 1518 cm<sup>−1</sup> 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.},
DOI = {10.32604/cmc.2022.025931}
}