Resistive switching in metal oxides is believed to be caused by a temperature and electric field driven redistribution of oxygen vacancies within a nanometer sized conductive filament. Accordingly, gaining detailed information about the chemical composition of conductive filaments is of key importance for a comprehensive understanding of the switching process. In this work, spectromicroscopy is used to probe the electronic structure of conductive filaments in Ta2O5‐based memristive devices. It is found that resistive switching leads to the formation of a conductive filament with an oxygen vacancy concentration of ≈20%. Spectroscopic insights provide detailed information about the chemical state of the tantalum cations and show that the filament is not composed of a metallic Ta0 phase. As an extreme case, devices after an irreversible dielectric breakdown are investigated. These devices feature larger conductive channels with higher oxygen vacancy concentrations. Using the experimental data as input for finite element simulations, the role of thermodiffusion for the formation process of conductive filaments is revealed. It is demonstrated that thermodiffusion is not the dominating effect for the filament formation here but might play a role in accelerating the forming process, as well as in the stabilization of the filament.

SummaryWe report on an experimental study of the charge transport through tunnel gaps formed by adjustable gold electrodes immersed into different solvents that are commonly used in the field of molecular electronics (ethanol, toluene, mesitylene, 1,2,4-trichlorobenzene, isopropanol, toluene/tetrahydrofuran mixtures) for the study of single-molecule contacts of functional molecules. We present measurements of the conductance as a function of gap width, conductance histograms as well as current–voltage characteristics of narrow gaps and discuss them in terms of the Simmons model, which is the standard model for describing transport via tunnel barriers, and the resonant single-level model, often applied to single-molecule junctions. One of our conclusions is that stable junctions may form from solvents as well and that both conductance–distance traces and current–voltage characteristics have to be studied to distinguish between contacts of solvent molecules and of molecules under study.

Memristive devices are novel electronic devices, which resistance can be tuned by an external voltage in a non-volatile way. Due to their analog resistive switching behavior, they are considered to emulate the behavior of synapses in neuronal networks. In this work, we investigate memristive devices based on the field-driven redox process between the p-conducting Pr0.7Ca0.3MnO3 (PCMO) and different tunnel barriers, namely, Al2O3, Ta2O5, and WO3. In contrast to the more common filamentary-type switching devices, the resistance range of these area-dependent switching devices can be adapted to the requirements of the surrounding circuit. We investigate the impact of the tunnel barrier layer on the switching performance including area scaling of the current and variability. Best performance with respect to the resistance window and the variability is observed for PCMO with a native Al2O3 tunnel oxide. For all different layer stacks, we demonstrate a spike timing dependent plasticity like behavior of the investigated PCMO cells. Furthermore, we can also tune the resistance in an analog fashion by repeated switching the device with voltage pulses of the same amplitude and polarity. Both measurements resemble the plasticity of biological synapses. We investigate in detail the impact of different pulse heights and pulse lengths on the shape of the stepwise SET and RESET curves. We use these measurements as input for the simulation of training and inference in a multilayer perceptron for pattern recognition, to show the use of PCMO-based ReRAM devices as weights in artificial neural networks which are trained by gradient descent methods. Based on this, we identify certain trends for the impact of the applied voltages and pulse length on the resulting shape of the measured curves and on the learning rate and accuracy of the multilayer perceptron.

Your electrode was Mo, his was C. You didn't discuss the mechanism, do you suppose it's a similar mechanism? Also the stoichiometry, do you get a similar stoichiometry as that for his samples, i.e. 1 : 1?