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.
Although multilevel capability is probably the most important property of resistive random access memory (RRAM) technology, it is vulnerable to reliability issues due to the stochastic nature of conducting filament (CF) creation. As a result, the various resistance states cannot be clearly distinguished, which leads to memory capacity failure. In this work, due to the gradual resistance switching pattern of TiO2−x-based RRAM devices, we demonstrate at least six resistance states with distinct memory margin and promising temporal variability. It is shown that the formation of small CFs with high density of oxygen vacancies enhances the uniformity of the switching characteristics in spite of the random nature of the switching effect. Insight into the origin of the gradual resistance modulation mechanisms is gained by the application of a trap-assisted-tunneling model together with numerical simulations of the filament formation physical processes.
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