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http://dx.doi.org/10.7471/ikeee.2022.26.4.633

Memristors based on Al2O3/HfOx for Switching Layer Using Single-Walled Carbon Nanotubes  

DongJun, Jang (Dept. of Electronics Engineering, Gangneung-Wonju National University)
Min-Woo, Kwon (Dept. of Electronics Engineering, Gangneung-Wonju National University)
Publication Information
Journal of IKEEE / v.26, no.4, 2022 , pp. 633-638 More about this Journal
Abstract
Rencently, neuromorphic systems of spiking neural networks (SNNs) that imitate the human brain have attracted attention. Neuromorphic technology has the advantage of high speed and low power consumption in cognitive applications and processing. Resistive random-access memory (RRAM) for SNNs are the most efficient structure for parallel calculation and perform the gradual switching operation of spike-timing-dependent plasticity (STDP). RRAM as synaptic device operation has low-power processing and expresses various memory states. However, the integration of RRAM device causes high switching voltage and current, resulting in high power consumption. To reduce the operation voltage of the RRAM, it is important to develop new materials of the switching layer and metal electrode. This study suggested a optimized new structure that is the Metal/Al2O3/HfOx/SWCNTs/N+silicon (MOCS) with single-walled carbon nanotubes (SWCNTs), which have excellent electrical and mechanical properties in order to lower the switching voltage. Therefore, we show an improvement in the gradual switching behavior and low-power I/V curve of SWCNTs-based memristors.
Keywords
Neuromorphic system; Resistive random-access memory; Spike-timing-dependent plasticity; Single-walled carbon nanotubes;
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