TY  - EJOU
AU  - Hwang, Sung Won 
AU  - Hong, Dae-Ki 

TI  - Flexible Memristive Devices Based on Graphene Quantum-Dot Nanocomposites
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 72
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Memristive devices; neuromorphic chip; resistive RAM; quantum dot; graphene

DO  - 10.32604/cmc.2022.025931