arXiv subjects

Nadia Jimenez Olalla

Publications and source records attributed to Nadia Jimenez Olalla.

4 recordsLinked to original sources

arXiv2026

High-speed time-series prediction using compact memristor circuits with adjustable dynamics

Ta$_2$O$_5$ nonvolatile memristors are used as compact, traceable, and well-controllable dynamic reservoir computing layers to perform time-series prediction tasks. The strongly voltage-dependent switching speed is utilized for information processing. It enables the configuration of tailorable programming and forgetting times in response to the positive and negative driving voltage pulses. Benchmarking this framework on time-series prediction problems reveals that the configurable forgetting dynamics enables a high prediction accuracy using a rather small number of memristive input channels. The training is based either on optimizing the output layer using linear regression with fixed forgetting times, or on optimizing the forgetting times as well. In the first case, six memristive channels, while in the second, only two memristive channels are used to demonstrate excellent prediction accuracy for the benchmark tasks. This scheme allows for the tunability of the operating frequency over many orders of magnitude: by adjusting the input voltage levels, the information processing speed of the same memristive dynamic layer can be increased from the kHz to the MHz range while maintaining excellent prediction accuracy. These findings demonstrate the merits of memristor based dynamic networks in the analysis, prediction and recovery of fast temporal signals, approaching telecommunication data rates.

cond-mat.mes-hall↗

arXiv2024

Benchmarking stochasticity behind reproducibility: denoising strategies in Ta$_2$O$_5$ memristors

Reproducibility, endurance, driftless data retention, and fine resolution of the programmable conductance weights are key technological requirements against memristive artificial synapses in neural network applications. However, the inherent fluctuations in the active volume impose severe constraints on the weight resolution. In order to understand and push these limits, a comprehensive noise benchmarking and noise reduction protocol is introduced. Our approach goes beyond the measurement of steady-state readout noise levels and tracks the voltage-dependent noise characteristics all along the resistive switching $I(V)$ curves. Furthermore, we investigate the tunability of the noise level by dedicated voltage cycling schemes in our filamentary Ta$_2$O$_5$ memristors. This analysis highlights a broad, order-of-magnitude variability of the possible noise levels behind seemingly reproducible switching cycles. Our nonlinear noise spectroscopy measurements identify a subthreshold voltage region with voltage-boosted fluctuations. This voltage range enables the reconfiguration of the fluctuators without resistive switching, yielding a highly denoised state within a few subthreshold cycles.

cond-mat.mes-hall↗

arXiv2024

Neural information processing and time-series prediction with only two dynamical memristors

Memristive devices are commonly benchmarked by the multi-level programmability of their resistance states. Neural networks utilizing memristor crossbar arrays as synaptic layers largely rely on this feature. However, the dynamical properties of memristors, such as the adaptive response times arising from the exponential voltage dependence of the resistive switching speed remain largely unexploited. Here, we propose an information processing scheme which fundamentally relies on the latter. We realize simple dynamical memristor circuits capable of complex temporal information processing tasks. We demonstrate an artificial neural circuit with one nonvolatile and one volatile memristor which can detect a neural spike pattern in a very noisy environment, fire a single voltage pulse upon successful detection and reset itself in an entirely autonomous manner. Furthermore, we implement a circuit with only two nonvolatile memristors which can learn the operation of an external dynamical system and perform the corresponding time-series prediction with high accuracy.

cond-mat.mes-hall↗

arXiv2022

Picosecond Time-Scale Resistive Switching Monitored in Real-Time

The resistance state of filamentary memristors can be tuned by relocating only a few atoms at interatomic distances in the active region of a conducting filament. Thereby the technology holds promise not only in its ultimate downscaling potential and energy efficiency but also in unprecedented speed. Yet, the breakthrough in high-frequency applications still requires the clarification of the dominant mechanisms and inherent limitations of ultra-fast resistive switching. Here we investigate bipolar, multilevel resistive switchings in tantalum pentoxide based memristors with picosecond time resolution. We experimentally demonstrate cyclic resistive switching operation due to 20 ps long voltage pulses of alternating polarity. Through the analysis of the real-time response of the memristor we find that the set switching can take place at the picosecond time-scale where it is only compromised by the bandwidth limitations of the experimental setup. In contrast, the completion of the reset transitions significantly exceeds the duration of the ultra-short voltage bias, demonstrating the dominant role of thermal diffusion and underlining the importance of dedicated thermal engineering for future high-frequency memristor circuit applications.

cond-mat.mes-hall↗