The operating principle of resistive random access memories (RRAMs) relies on the distribution of ionic species and their influence on the electron transport. Taking into account that formation and annihilation of conducting filaments (CFs) is the driving mechanism for the switching effect, it is very important to control the regions where these filaments will evolve. Thus, homolayers of titanium oxide with different oxygen contents were fabricated in order to tune the local electrical and thermal properties of the CFs and narrow down the potential percolation paths. We show that the oxygen content in the top layer of the TiO2−x/TiO2−y bilayer memristors can directly influence the morphology of the layers which affect the diffusion barrier and consequently the diffusivity and drift velocity of oxygen vacancies, yielding in important enhancement of switching characteristics, in terms of spatial uniformity (σ/μ < 0.2), enlarged switching ratio (∼104), and synaptic learning. In order to address the experimental data, a physical model was applied, divulging the crucial role of temperature, electric potential and oxygen vacancy density on the switching effect and offering physical insights to the SET/RESET transitions and the analog switching. The forming free nature of all the devices in conjunction with the self-rectifying behavior, should also be regarded as important assets towards RRAM device optimization.

The threshold switching effect is considered of outmost importance for a variety of applications ranging from the reliable operation of crossbar architectures to emulating neuromorphic properties with artificial neural networks. This property is strongly believed to be associated with the rich inherit dynamics of a metallic conductive filament (CF) formation and its respective relaxation processes. Understanding the origin of these dynamics is very important in order to control the degree of volatility and design novel electronic devices. Here, we present a synergistic numerical and experimental approach in order to deal with that issue. The distribution of relaxation time is addressed through time-resolved pulse measurements whereas the entire switching behavior is modeled through a 2D dynamical model by taking into account the destructive interference of the drift/diffusion transport mechanisms and the Soret diffusion flux due to the intense local Joule heating. The proposed mechanism interprets successfully both the threshold to bipolar switching transition as well as the self-rectifying effects in SiO2-based memories. The model incorporates the effect of electrode materials on the switching pattern and provides a different perception of the ionic transport processes, shading light into the ultra-small lifetimes of the CF and explaining the different behavior of the silver or copper active materials in a conductive bridge random access memory architecture.

The resistive switching characteristics of forming free TiO2 – x/TiO2 – y memory devices containing Pt nanocrystals (NCs) beneath the top electrode were systematically investigated through experiments and numerical simulation insights. By embedding Pt nanocrystals, we have the possibility to narrow down the possible locations where the switching effect will evolve and thus significantly improve the inherent variability of the devices. Besides, the deployment of bilayer structures can tune the resistance levels, since the presence of the layer with low oxygen content (TiO2 – y) acts practically as series resistance, limiting the operating currents and at the same time forcing the switching effect to evolve in the layer with the higher oxygen content (TiO2 – z). A numerical model is implemented, in order to shed light into the origin of the SET/RESET transitions and illustrate the direct impact of NCs on the conducting filament (CF) shape and distribution of oxygen vacancies. It is demonstrated that a higher density of oxygen vacancies is created in the vicinity of NCs, which can directly impact the operating current values and the uniformity of the switching characteristics. The presence of NCs also facilitates the reduction of the operating voltages (∼3 V), and, as a result, it significantly improves power consumption, without sacrificing the switching ratio (∼103), temporal/spatial variability (σ/μ < 0.2), and pulse endurance (108 cycles) characteristics of our memory cells. Evidence about the impact of the NCs position within the material configuration are also presented. The direct impact of Pt NCs on the depression and potentiation characteristics of the synaptic weight denotes similarly the huge applicability of our approach to tune a wide range of resistive switching properties.

The development of alternative brain-inspired neuromorphic computing architectures is anticipated to play a key role in addressing the strict requirements of the artificial intelligence era. In order to obtain a high degree of learning accuracy within an artificial neural network (ANN) that operates with the backpropagation algorithm, a highly symmetric synaptic weight distribution is desired. Along these lines, we present here a detailed device engineering approach that enables analog synaptic properties in completely forming free SiO2-conductive bridge memories. This is achieved by either incorporating a dense layer of Pt nanoparticles as a bottom electrode or fabricating bilayer structures using a second switching layer of VOx. Interestingly, compared with the reference sample that manifests both threshold and bipolar switching modes, the Pt NC sample exhibits only the threshold switching pattern, whereas the bilayer configuration operates only under the bipolar switching mode, as illustrated by direct current measurements. These characteristics have a direct, while different impact, on the conductance modulation pattern and determine the analog nature of the synaptic weight distribution. Valuable insights regarding the origin of these effects and, in particular, of the symmetric and linear conductance modulation processes are gained through the implementation of a self-consistent numerical model that takes into account both the impact of the electrodes' thermal conductivity on the switching pattern and the different diffusion barriers for silver ion migration. Our approach provides useful guidelines toward the realization of high yield ANNs with biological-like dynamic behavior by controlling the conducting filament growth mechanism.

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