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R. Leal Martir

Publications and source records attributed to R. Leal Martir.

4 recordsLinked to original sources

Engineering Synaptic Dynamics in Ag-Modified TaO$_x$ Memristive Devices

Engineering not only the magnitude but also the dynamics of synaptic weight updates is an important challenge for memristive neuromorphic hardware. Here, we show that Ag modification of TaO$_x$ memristors introduces an electrically selectable degree of freedom in synaptic depression through the interplay between Ag-related and oxygen-vacancy dynamics. Reference devices exhibit conventional bipolar switching, whereas Ag-modified devices display symmetric table-with-legs hysteresis loops, metastable intermediate states, and a strongly non-monotonic dependence of depression dynamics on programming amplitude. By varying only the pulse amplitude, the same device can be driven among three regimes: gradual sigmoidal depression, an abrupt update concentrated within a few pulses, and a broadly distributed evolution extending over more than one hundred pulses. A minimal coupled-state model, in which a vacancy-related switching variable interacts with a slower Ag-related internal degree of freedom, reproduces this gradual--abrupt--gradual crossover using two individually monotonic field-activated processes. The functional impact is assessed in a memristor-based multilayer perceptron. For MNIST, the abrupt, sigmoidal, and slowly evolving responses yield accuracies of approximately 50%, 85%, and 88%, respectively, while the gradual regime reaches approximately 72% for Fashion-MNIST. These results show that coupling Ag-related and oxygen-vacancy dynamics can transform synaptic depression from a fixed device characteristic into an electrically programmable property.

cond-mat.mtrl-sci↗

Tuning the memristive response of TaO$_x$-based devices with Ag Nanoparticles

Defect engineering is a key strategy to control resistive switching (RS) in oxide-based memristive devices, where oxygen vacancy (OV) dynamics governs filament formation and rupture. We investigate the effect of Ag nanoparticles (AgNPs) embedded in the top electrode of Pt/Ta2O5/TaO2/Pt memristors and analyze their RS behavior and statistical stability. Devices without AgNPs exhibit two hysteresis switching loops (HSLs) with opposite chiralities, originating from the participation of the Pt/Ta2O5 top interface and the Ta2O5/TaO2 bottom interface. Incorporating AgNPs reduces the overall device resistance and selectively suppresses one loop, yielding a single, well-defined switching mode. Moreover, devices incorporating Ag-NPs show markedly reduced cycle-to-cycle variability of the high-resistance state, as confirmed by Weibull analysis, indicating improved endurance and switching reproducibility. Within a filamentary RS framework, we attribute this behavior to local metallization of the top interface by AgNPs, which partially inhibit OV transport and confines the RS dynamics to the bottom interface. Numerical simulations with the Oxygen Vacancy Resistive Network (OVRN) model succesfully reproduce the experimental HSLs, statistical trends, and tunable ON/OFF ratios with AgNPs coverage. These findings demonstrate that targeted interface metallization via metallic nanoparticles provides an effective route to control multi-interface RS dynamics and improve switching stability in without modifying the oxide architecture.

cond-mat.mes-hall↗

On the origin of filamentary resistive switching in oxides-based memristive devices

The control and manipulation of filamentary resistive switching (FRS) is essential for practical applications in fields like non-volatile memories and neuromorphic computing. However, key aspects of the dynamics of conductive filament formation and their influence on device resistance remain incompletely understood. In this work we study FRS in binary oxides based memristors by investigating the dynamics of oxygen vacancies (OV) on a two dimensional lattice and their role in forming low-resistance paths that facilitate the transition between high and low global resistance states. We reveal that the mere formation of an OV percolation path is insufficient to induce a transition to a low-resistance state. Instead, an OV concentration exceeding a critical threshold across all sites in the filament is required to generate a low-resistivity conducting path. Furthermore, we simulate the impact of static defects -which block OV migration and would correspond to voids in real porous samples-, on filament formation. We show that there is a range of defect density values where OV percolate through the sample, leading to the formation of OV filaments, but conductive paths remain absent. Additionally, a small concentration of defects can reduce the final value of the low-resistance state, thereby increasing the ON-OFF ratio. These findings provide valuable insights into optimizing defective nanomaterials with memristive properties, which are crucial for advancing in-memory and neuromorphic computing technologies.

cond-mat.mes-hall↗

Oxygen vacancies kinetics in $TaO_{2-h}$/$Ta_{2}$$O_{5-x}$ memristive interfaces

Oxygen vacancies (OV) are pervasive in metal oxides and play a pivotal role in the switching behaviour of oxide-based memristive devices. In this work we address, through a combination of experiments and theoretical simulations, OV dynamics in $Pt/TaO_{2-h}/Ta_{2}O_{5-x}/TaO_${2-y}$/Pt$ devices. In particular, we focus on the RESET transition (from low to high resistance), induced by the application of electrical pulse(s), by choosing different initial OV profiles and studying their kinetics during the mentioned process. We demonstrate that by selecting specific OV profiles it is possible to tune the characteristic time-scale of the RESET. Finally, we show that the implementation of gradual RESETs, induced by applying many (small) successive pulses, allows estimating the activation energies involved in the OV electromigration process. Our results help paving the way for OV engineering aiming at optimizing key memristive figures such as switching speed or power consumption, which are highly relevant for neuromorphic or in-memory computing implementations.

cond-mat.mes-hall↗