Synaptic Device based on Resistive Switching Memory using Single-walled Carbon Nanotubes

Recently, research has been conducted on a biomimetic system called artificial neural networks (ANNs) to overcome the limits of Von Neumann’s system. Spiking neural networks (SNNs) in ANNs are hardware systems that implement NNs’ low-power parallel processing. The electrical characteristics of synaptic devices, which affect learning and memory, are important in SNN construction. Synaptic devices based on resistive switching are called memristors and have sufficient learning capabilities, such as spike-timing-dependent plasticity gradual switching [1]. However, memristors require high switching voltages and currents, resulting in high power consumption. To reduce the operation voltage of the synapse, new materials must be developed for the switching oxide and metal electrode. The conventional memristor structure comprises metal-oxide-metal (MOM) or metal-oxide-N⁺ Si (MOS). This study proposes a synaptic device with single-walled carbon nanotubes (SWCNTs) with excellent electrical and mechanical properties to lower the switching voltage and fabricate a metal-oxide-SWCNTs-N⁺ Si (MOCS)-structured synaptic device using SWCNTs as a metal electrode, as shown in Fig. 1 [2]. Finally, the electrical characteristics of MOM, MOS, and MOCS structures are analyzed and compared.