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.

Herein, an investigation of the impact of the dopant and carbon content in iron-doped zinc oxide/carbon composites is presented. For this purpose, a comprehensive morphological, structural, and electrochemical characterization of a series of different compounds is reported, including techniques like X-ray diffraction (XRD), transmission electron microscopy (TEM), inductively coupled plasma optical emission spectroscopy (ICP-OES), thermogravimetric analysis (TGA), specific surface area using the Brunauer-EmmettTeller (BET) algorithm, pycnometry, small-angle X-ray scattering (SAXS), cyclic voltammetry (CV), and galvanostatic cycling. The obtained results reveal an impact of the iron-dopant content on the crystallite and particle size as well as the detailed de-/lithiation mechanism. The effect on the cycling stability, however, appears to be rather minor. The carbon coating content, on the contrary, has a significant influence on the cycling stability and rate capability. According to these results, a carbon content of about 10 wt% is sufficient to achieve stable cycling at lower current densities, while a carbon content of 15-20 wt% allows for specific capacities of 425-500 mAh g −1 , when applying a specific current of 1 A g −1 , for instance. Despite the tremendous commercial success of lithium-ion batteries for a wide variety of applications -ranging from small-scale portable electronics to electric bikes and scooters and, recently, electric vehicles -further enhanced energy and power densities are needed for the realization of a fully electrified public and private transport. [1][2][3][4] While optimized cell designs and battery engineering have a great impact on such improvement, the next great leap forward will require the implementation of new battery chemistries. With regard to the anode side, most research activities within the past years have focused on replacing graphite by alloying or conversion materials, which commonly allow for substantially higher specific capacities. [5][6][7] However, both material classes suffer intrinsic challenges like dramatic volume variations upon de-/lithiation (particularly in case of alloying materials) and relatively wide lithium reaction potentials, accompanied by a substantial voltage hysteresis between charge and discharge (especially in case of conversion materials), resulting in rapid capacity fading of the corresponding electrodes, comparably lower specific energies, and improvable energy storage efficiencies, respectively. [5][6][7] In an attempt to overcome these challenges, we have recently reported a new class of materials -transition metal-doped metal oxides -for which the reduced transition metal (i.e., upon lithiation) enables the reversible formation of Li 2 O, i.e., the conversion reaction. Additionally, the metal itself can form a lithium alloy. [8][9][10][11][12] Within this materials' class a particular focus was, so far, set on Fedoped ZnO with a Zn:Fe ratio of 9:1, providing a theoretical specific capacity of 966 mAh g -1 . 8,9,12 While the nanopart...

Memristive switching devices are promising for future data storage and neuromorphic computing applications to overcome the scaling and power dissipation limits of classical CMOS technology. Many groups have engineered bilayer oxide structures to enhance the switching performance especially in terms of retention and device reliability. Here, introducing retention enhancement oxide layers into the memristive stack is shown to result in a reduction of the switching speed not only by changing the voltage and temperature distribution in the cell, but also by influencing the rate‐limiting‐step of the switching kinetics. In particular, it is demonstrated that by introducing a retention enhancement layer into resistive switching SrTiO3 devices, the kinetics are no longer determined by the interface exchange reaction between switching oxide and active electrode, but depend on the oxygen ion migration in the additional interface layer. Thus, the oxygen migration barrier in the additional layer determines the switching speed. This trade‐off between retention and switching speed is of general importance for rational engineering of memristive devices.

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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.

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