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Resistive random access memory (ReRAM), a new emerging nonvolatile memory technology based on changes in electrical resistivity of a dielectric film, offers promising advantages such as scalability, fast switching, and low operation voltage. However, for ReRAM to become a successful technology, it is necessary to accurately control the stochastic nature of the conductive nanoscale filaments (CNFs) that governs the resistive switching (RS) behavior of the device and limits its long-term stability and reliability. In this paper, we developed a highly scalable nanostructured/textured electrode that is composed of an array of Al nanotips based on an anodic aluminum oxide template. The nanotips improve the RS characteristics by intensifying the electric field at the apex of each nanotip which is demonstrated using numerical simulations. The localized electric field induces the repetitive nucleation/ formation/rupture of the CNFs in a more controlled fashion compared to a flat Al electrode. As a result, the nanotip sample exhibits uniform and reduced forming/reset voltages as low as 4.70 ± 0.98V/1.00 ± 0.19 V, stable endurance, and long-term retention. As a result, we were able to achieve ultralow-power and error-free operation of 100 cells covering a large area, significantly demonstrating improved uniformity and reliability compared to devices made using flat Al electrodes. This universal bottom-up strategy of self-organized nanostructured-electrodes provides a pathway toward large-scale, highly reliable, and RS memory devices. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the linear conductance-change characteristics of a conductive-bridging RAM (CBRAM) to be employed as an artificial synapse device in neuromorphic systems. The CBRAM with a bilayer electrolyte structure (${\mathrm {Cu/Cu}}_{{2}-{x}}\text{S}$ / $\mathrm {WO}_{{3}-{x}}$ /W) exhibits analog switching behavior during the depression process due to the well-controlled dissolution of the conductive filament. To analyze the origin of this motion, we investigate the effective voltage applied to $\mathrm {Cu}_{{2}-{x}}\text{S}$ and $\mathrm {WO}_{{3}-{x}}$. Our findings reveal that $\mathrm {Cu}_{{2}-{x}}\text{S}$ , acting as a voltage divider, helps in suppressing the large voltage drop in $\mathrm {WO}_{{3}-{x}}$ , where the formation/dissolution of filament occurs. Furthermore, due to the diode-like characteristics of $\mathrm {Cu}_{{2}-{x}}\text{S}$ and the division of voltage drop between $\mathrm {WO}_{{3}-{x}}$ and $\mathrm {Cu}_{{2}-{x}}\text{S}$ , an optimum programming energy is applied to $\mathrm {WO}_{{3}-{x}}$ during the depression process. This leads to linear conductance-change characteristics under identical pulses. However, abrupt conductance-change characteristics are observed during the potentiation process. Thus, we use only the device characteristics of the depression part for the neuromorphic system. An excellent classification accuracy is achieved due to the linear conductance-change characteristics and optimized pulse conditions. [ABSTRACT FROM AUTHOR]