arXiv subjects

Tímea Nóra Török

Publications and source records attributed to Tímea Nóra Török.

8 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.

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arXiv2026

Voltage-driven transition from steady-state fluctuations to phase-transition noise in nanoscale VO$_2$ devices

We investigate the electrically driven metal-to-insulator transition (MIT) in nanoscale vanadium dioxide (VO$_2$) Mott memristor through noise spectroscopy and two-dimensional resistor network simulations. Our experiments focus on both the insulating phase as the applied voltage approaches the threshold voltage (set transition) and the metallic phase as the voltage is reduced toward the reset voltage (reset transition). In both regimes, we observe an order of magnitude increase in relative current noise near the transition points. To analyze the origin of this noise enhancement, we use simulations that capture the stochastic dynamics of the phase transition. The simulations indicate that the increased noise stems from amplified phase fluctuations near the percolation threshold, where competing metallic and insulating domains lead to dynamic reconfiguration of the conduction paths. In addition, we show that the precursor current fluctuations observed near the switching threshold are consistent with the threshold voltage variability measured in repeated switching cycles, indicating that the noise sets a lower bound on the achievable variance. These findings offer key insights into the non-equilibrium processes governing phase transitions in nanoscale VO$_2$ devices under electrical stimuli.

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arXiv2025

Memristor-Driven Spike Encoding for Fully Implantable Cochlear Implants

Objective: This work aims to demonstrate a low-power, biomimetic auditory sensing concept for fully implantable cochlear implants. The approach draws inspiration from the frequency selectivity and temporal encoding of the cochlea, and uses neuromorphic spike generation to replace conventional signal processing blocks. The goal is to establish a compact, energy-efficient front-end architecture suitable for future implantable systems. Methods: An auditory sensing unit was implemented, consisting of a piezoelectric MEMS cantilever mechanically coupled to a single VO$_2$ nanogap Mott memristor-based oscillator. This configuration enables FFT-free, frequency-selective sensing and direct spike generation, forming a biomimetic auditory front end. The concept was experimentally examined using controlled mechanical excitation. Results: The sensing unit exhibited frequency-selective detection of mechanical vibrations in the nanometer to tens-of-nanometers displacement range and generated biomimetic spiking waveforms. Spike rate-encoding of the input amplitude was demonstrated, with output spiking frequencies tunable between approximately 100 Hz and 1 kHz depending on the excitation level. The waveform was finally converted to a biphasic shape suitable for cochlear implant stimulation. Significance: Temporal encoding is fundamental to natural auditory signal processing in the nervous system. By implementing this principle through neuromorphic spike encoding, the proposed approach can provide significant benefits for cochlear implants. In addition, the circuit has the potential to reduce footprint, energy consumption, and latencies compared with current commercial solutions.

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arXiv2025

VO$_2$ oscillator circuits optimized for ultrafast, 100 MHz-range operation

Oscillating neural networks are promising candidates for a new computational paradigm, where complex optimization problems are solved by physics itself through the synchronization of coupled oscillating circuits. Nanoscale VO$_2$ Mott memristors are particularly promising building blocks for such oscillating neural networks. Until now, however, not only the maximum frequency of VO$_2$ oscillating neural networks, but also the maximum frequency of individual VO$_2$ oscillators has been severely limited, which has restricted their efficient and energy-saving use. In this paper, we show how the oscillating frequency can be increased by more than an order of magnitude into the 100 MHz range by optimizing the sample layout and circuit layout. In addition, the physical limiting factors of the oscillation frequencies are studied by investigating the switching dynamics. To this end, we investigate how much the set and reset times slow down under oscillator conditions compared to the fastest switching achieved with single dedicated pulses. These results pave the way towards the realization of ultra-fast and energy-efficient VO$_2$-based oscillating neural networks.

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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.

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arXiv2023

Noise tailoring, noise annealing and external noise injection strategies in memristive Hopfield neural networks

The commercial introduction of a novel electronic device is often preceded by a lengthy material optimization phase devoted to the suppression of device noise as much as possible. The emergence of novel computing architectures, however, triggers a paradigm change in noise engineering, demonstrating that a non-suppressed, but properly tailored noise can be harvested as a computational resource in probabilistic computing schemes. Such strategy was recently realized on the hardware level in memristive Hopfield neural networks delivering fast and highly energy efficient optimization performance. Inspired by these achievements we perform a thorough analysis of simulated memristive Hopfield neural networks relying on realistic noise characteristics acquired on various memristive devices. These characteristics highlight the possibility of orders of magnitude variations in the noise level depending on the material choice as well as on the resistance state (and the corresponding active region volume) of the devices. Our simulations separate the effects of various device non-idealities on the operation of the Hopfield neural network by investigating the role of the programming accuracy, as well as the noise type and noise amplitude of the ON and OFF states. Relying on these results we propose optimized noise tailoring, noise annealing, and external noise injection strategies.

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arXiv2023

Quantum transport properties of tantalum-oxide resistive switching filaments

Filamentary resistive switching devices are not only considered as promising building blocks for brain-inspired computing architectures, but they also realize an unprecedented operation regime, where the active device volume reaches truly atomic dimensions. Such atomic-sized resistive switching filaments represent the quantum transport regime, where the transmission eigenvalues of the conductance channels are considered as a specific device fingerprint. Here, we gain insight into the quantum transmission properties of close-to-atomic-sized resistive switching filaments formed across an insulating Ta$_2$O$_5$ layer through superconducting subgap spectroscopy. This method reveals the transmission density function of the open conduction channels contributing to the device conductance. Our analysis confirms the formation of truly atomic-sized filaments composed of 3-8 Ta atoms at their narrowest cross-section. We find that this diameter remains unchanged upon resistive switching. Instead, the switching is governed by the redistribution of oxygen vacancies within the filamentary volume. The set/reset process results in the reduction/formation of an extended barrier at the bottleneck of the filament which enhances/reduces the transmission of the highly open conduction channels.

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arXiv2020

Breaking the quantum PIN code of atomic synapses

Atomic synapses represent a special class of memristors whose operation relies on the formation of metallic nanofilaments bridging two electrodes across an insulator. Due to the magnifying effect of this narrowest cross-section on the device conductance, a nanometer scale displacement of a few atoms grants access to various resistive states at ultimately low energy costs, satisfying the fundamental requirements of neuromorphic computing hardware. Yet, device engineering lacks the complete quantum characterization of such filamentary conductance. Here we analyze multiple Andreev reflection processes emerging at the filament terminals when superconducting electrodes are utilized. Thereby the quantum PIN code, i.e. the transmission probabilities of each individual conduction channel contributing to the conductance of the nanojunctions is revealed. Our measurements on Nb$_2$O$_5$ resistive switching junctions provide a profound experimental evidence that the onset of the high conductance ON state is manifested via the formation of truly atomic-sized metallic filaments.

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