Nonpolar Resistive Switching in Ag@TiO2 Core-Shell Nanowires

The Effect of Nitrogen Annealing on the Resistive Switching Characteristics of the W/TiO2/FTO Memory Device

In this paper, the effect of nitrogen annealing on the resistive switching characteristics of the rutile TiO₂ nanowire-based W/TiO₂/FTO memory device is analyzed. The W/TiO₂/FTO memory device exhibits a nonvolatile bipolar resistive switching behavior with a high resistance ratio (R_HRS/R_LRS) of about two orders of magnitude. The conduction behaviors of the W/TiO₂/FTO memory device are attributed to the Ohmic conduction mechanism and the Schottky emission in the low resistance state and the high resistance state, respectively. Furthermore, the R_HRS/R_LRS of the W/TiO₂/FTO memory device is obviously increased from about two orders of magnitude to three orders of magnitude after the rapid nitrogen annealing treatment. In addition, the change in the W/TiO₂ Schottky barrier depletion layer thickness and barrier height modified by the oxygen vacancies at the W/TiO₂ interface is suggested to be responsible for the resistive switching characteristics of the W/TiO₂/FTO memory device. This work demonstrates the potential applications of the rutile TiO₂ nanowire-based W/TiO₂/FTO memory device for high-density data storage in nonvolatile memory devices.

Femtosecond Laser-Induced Nano-Joining of Volatile Tellurium Nanotube Memristor

Nanowire/nanotube memristor devices provide great potential for random-access high-density resistance storage. However, fabricating high-quality and stable memristors is still challenging. This paper reports multileveled resistance states of tellurium (Te) nanotube based on the clean-room free femtosecond laser nano-joining method. The temperature for the entire fabrication process was maintained below 190 °C. A femtosecond laser joining technique was used to form nanowire memristor units with enhanced properties. Femtosecond (fs) laser-irradiated silver-tellurium nanotube-silver structures resulted in plasmonic-enhanced optical joining with minimal local thermal effects. This produced a junction between the Te nanotube and the silver film substrate with enhanced electrical contacts. Noticeable changes in memristor behavior were observed after fs laser irradiation. Capacitor-coupled multilevel memristor behavior was observed. Compared to previous metal oxide nanowire-based memristors, the reported Te nanotube memristor system displayed a nearly two-order stronger current response. The research displays that the multileveled resistance state is rewritable with a negative bias.

Evolution and modulation of Ag filament dynamics within memristive devices based on necklace-like Ag@TiO2nanowire networks

Random nanowire networks (NWNs) are regarded as promising memristive materials for applications in information storage, selectors, and neuromorphic computing. The further insight to understand their resistive switching properties and conduction mechanisms is crucial to realize the full potential of random NWNs. Here, a novel planar memristive device based on necklace-like structure Ag@TiO₂NWN is reported, in which a strategy only using water to tailor the TiO₂shell on Ag core for necklace-like core-shell structure is developed to achieve uniform topology connectivity. With analyzing the influence of compliance current on resistive switching characteristics and further tracing evolution trends of resistance state during the repetitive switching cycles, two distinctive evolution trends of low resistance state failure and high resistance state failure are revealed, which bear resemblance to memory loss and consolidation in biological systems. The underlying conduction mechanisms are related to the modulation of the Ag accumulation dynamics inside the filaments at cross-point junctions within conductive paths of NWNs. An optimizing principle is then proposed to design reproducible and reliable threshold switching devices by tuning the NWN density and electrical stimulation. The optimized threshold switching devices have a high ON/OFF ratio of ∼10⁷with threshold voltage as low as 0.35 V. This work will provide insights into engineering random NWNs for diverse functions by modulating external excitation and optimizing NWN parameters to satisfy specific applications, transforming from neuromorphic systems to threshold switching devices as selectors.

Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks

Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.

Planar and Transparent Memristive Devices Based on Titanium Oxide Coated Silver Nanowire Networks with Tunable Switching Voltage

Threshold switching devices are fundamental active elements in more than Moore approaches, integrating the new generation of non-volatile memory devices. Here, the authors report an in-plane threshold resistive switching device with an on/off ratio above 10⁶ , a low resistance state of 10 to 100 kΩ and a high resistance state of 10 to 100 GΩ. Our devices are based on nanocomposites of silver nanowire networks and titanium oxide, where volatile unipolar threshold switching takes place across the gap left by partially spheroidized nanowires. Device reversibility depends on the titanium oxide thickness, while nanowire network density determines the threshold voltage, which can reach as low as 0.16 V. The switching mechanism is explained through percolation between metal-semiconductor islands, in a combined tunneling conduction mechanism, followed by a Schottky emission generated via Joule heating. The devices are prepared by low-cost, atmospheric pressure, and scalable techniques, enabling their application in printable, flexible, and transparent electronics.

In Situ Current-Accelerated Phase Cycling with Metallic and Semiconducting Switching in Copper Nanobelts at Room Temperature

Here, a current-accelerated phase cycling by an in situ current-induced oxidation process was demonstrated to reversibly switch the local metallic Cu and semiconducting Cu₂O phases of patterned polycrystalline copper nanobelts. Once the Cu nanobelts were applied by a direct-current bias of ∼0.5 to 1 V in air with opposite polarities, the resistance between several hundred ohms and more than MΩ can be manipulated. In practice, the thickness of 60 nm with a moderate grain size inhibiting both electromigration and permanent oxidation is the optimized condition for reversible switching when the oxygen supply is sufficient. More than 40% of the copper localized beneath the positively biased electrode was oxidized assisted by the Joule heating, blocking the current flow. On the contrary, the reduction reaction of Cu₂O was activated by the thermally assisted electromigration of Cu atoms penetrating the interlayer at the reverse bias. Finally, based on a high on/off ratio, the fast switching and the scalable production, reusable feasibility based on copper nanobelts such as the memristor array was demonstrated.

Sleep-Dependent Memory Consolidation in a Neuromorphic Nanowire Network

A neuromorphic network composed of silver nanowires coated with TiO₂ is found to show certain parallels with neural networks in nature such as biological brains. Owing to the memristive properties emerging at nanowire-to-nanowire contacts, where the Ag/TiO₂/Ag interface exists, the network can store information in the form of connectivity between nanowires in the network as electrically measured as an increase in conductance. The observed memory arises from an interplay between the topological constraints imposed by a complex network structure and the plasticity of its constituting memristive Ag/TiO₂/Ag junctions. Regarding the long-term decay of the connectivity in the network, we further investigate the controllability of the established connectivity. Inspired by the regulated activity cycles of the human brain during sleep, a learning-sleep-recovery cycle was mimicked by applying voltage pulses, with controlling pulse heights and duty ratios, to the nanowire network. Interestingly, even when the conductance was lost during sleep, the network could quickly recover previous states of conductance in the recovery process after sleep. Comparison between results of experiments and theoretical simulations revealed that such a quick recovery of conductance can be realized by sparse voltage pulse application during sleep; in other words, sleep-dependent memory consolidation occurs and can be controlled. The present results provide clues to new learning designs in neuromorphic networks for achieving longer memory retention for future neuromorphic technology.

The Electro-Optical Performance of Silver Nanowire Networks

Networks of metallic nanowires have the potential to meet the needs of next-generation device technologies that require flexible transparent conductors. At present, there does not exist a first principles model capable of predicting the electro-optical performance of a nanowire network. Here we combine an electrical model derived from fundamental material properties and electrical equations with an optical model based on Mie theory scattering of light by small particles. This approach enables the generation of analogues for any nanowire network and then accurately predicts, without the use of fitting factors, the optical transmittance and sheet resistance of the transparent electrode. Predictions are validated using experimental data from the literature of networks comprised of a wide range of aspect ratios (nanowire length/diameter). The separation of the contributions of the material resistance and the junction resistance allows the effectiveness of post-deposition processing methods to be evaluated and provides a benchmark for the minimum attainable sheet resistance. The predictive power of this model enables a material-by-design approach, whereby suitable systems can be prescribed for targeted technology applications.

Catalysis of Organic Pollutants Abatement Based on Pt-Decorated Ag@Cu2O Heterostructures

Pt-decorated Ag@Cu2O heterostructures were successfully synthesized using a simple and convenient method. The Pt nanoparticle density on the Ag@Cu2O can be controlled by changing the concentration of the Pt precursor. The synthesized Ag@Cu2O-Pt nanoparticles exhibited excellent catalytic performance, which was greatly affected by changes in the Ag@Cu2O-Pt structure. To optimize the material's properties, the synthesized Ag@Cu2O-Pt nanoparticles were used to catalyze toxic pollutants and methyl orange (MO), and nontoxic products were obtained by catalytic reduction. The Pt-decorated Ag@Cu2O nanoparticles showed excellent catalytic activity, which significantly decreased the pollutant concentration when the nanoparticles were used for catalytic reduction. The redistribution of charge transfer is the nanoparticles' main contribution to the catalytic degradation of an organic pollutant. This Pt-decorated Ag@Cu2O material has unique optical and structural characteristics that make it suitable for photocatalysis, local surface plasmon resonance, and peroxide catalysis.

Low-cost fabrication of flexible transparent electrodes based on Al doped ZnO and silver nanowire nanocomposites: impact of the network density

We report the study of nanocomposite transparent electrodes based on aluminium doped zinc oxide (ZnO : Al) thin films and silver nanowire (AgNW) networks. The electrodes are fully fabricated by low-cost, open-air techniques, namely, atmospheric pressure spatial atomic layer deposition and spray coating. We show that the transparency and the electrical conductivity of the ZnO : Al/AgNW nanocomposites can be tuned by controlling the AgNW network density. We also demonstrate that the thermal, electrical and mechanical stabilities of the nanocomposites are drastically enhanced compared to those of AgNW networks or ZnO : Al thin films separately. Interestingly, we report a clear continuous decrease of the electrical resistance of the nanocomposites for network densities even below the percolation threshold. We propose a model to explain the relationship between the conductivity of the nanocomposites and the AgNW network density. Our physical model is based on the non-negligible contribution of percolating clusters of AgNWs for network densities below the percolation threshold. Our results provide a means to predicting the physical properties of such nanocomposites for applications in solar cells and other optoelectronic devices. Finally, the deposition methods used open the way towards stable, low-cost and flexible transparent electrodes for industrial applications.

Emergence of winner-takes-all connectivity paths in random nanowire networks

Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a "winner-takes-all" conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing.