Stability of quantized conductance levels in memristors with copper filaments: toward understanding the mechanisms of resistive switching

Memristors are among the most promising elements for modern microelectronics, having unique properties such as quasi-continuous change of conductance and long-term storage of resistive states. However, identifying the physical mechanisms of resistive switching and evolution of conductive filaments in such structures still remains a major challenge. In this work, aiming at a better understanding of these phenomena, we experimentally investigate an unusual effect of enhanced conductive filament stability in memristors with copper filaments under the applied voltage and present a simplified theoretical model of the effect of a quantum current through a filament on its shape. Our semi-quantitative, continuous model predicts, indeed, that for a thin filament, the "quantum pressure" exerted on its walls by the recoil of charge carriers can well compete with the surface tension and crucially affect the evolution of the filament profile at the voltages around 1V. At lower voltages, the quantum pressure is expected to provide extra stability to the filaments supporting quantized conductance, which we also reveal experimentally using a novel methodology focusing on retention statistics. Our results indicate that the recoil effects could potentially be important for resistive switching in memristive devices with metallic filaments and that taking them into account in rational design of memristors could help achieve their better retention and plasticity characteristics.Comment: version accepted for publication in Phys. Rev. Applied, including improved statistic

Paramagnetic properties of carbon-doped titanium dioxide

This paper reports the experimental results on paramagnetic properties of carbon-doped titanium dioxide. The electron paramagnetic resonance study of the samples has been carried out both in dark and under illumination. The nature of defects and their dynamics under illumination of carbon-doped TiO(2) samples is discussed

Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain‐inspired computing systems built with partially unreliable analog elements