arXiv subjects

András Halbritter

Publications and source records attributed to András Halbritter.

At least 19 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

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.

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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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arXiv2024

Improving single-molecule conductance measurements with change point detection from the econometrics toolbox

Structural breaks occur in timeseries data across a broad range of fields, from economics to nanosciences. For measurements of single-molecule break junctions, structural breaks in conductance versus displacement data occur when the molecular junction ruptures. This moment is significant because the molecule is likely in its most extended geometry, and therefore resembles most closely the geometry used in theoretical predictions. Conventional single-molecule break junction data analysis, on the other hand, typically uses the entire molecular plateau to estimate the single-molecule conductance, which skews the estimate when the plateau is sloped. Borrowing from econometrics, where the study of structural breaks is well established, we present change point detection (CPD) as a tool to search for junction rupture in single-molecule break junction data, and improve estimates in single-molecule conductance. We demonstrate that using CPD instead of the conventional 1D conductance histogram to determine the mean molecular conductance yields a standard deviation in the estimate of typically half that of the conventional approach, greatly improving accuracy. We apply CPD to three separate data sets, two on 4,4'-bipyridine and one on a silane, two at room temperature and one at 4 K, two in one lab, one in another, to show the wide applicability of even the simplest of CPD algorithms: the Chow test. This versatility and better accuracy will propagate into more accurate theoretical simulations. These improved metrics, in turn, will further improve any downstream analyses, including all emerging machine learning approaches.

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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

Voltage-time dilemma and stochastic threshold voltage variation in pure silver atomic switches

The formation and dissolution of silver nanowires plays a fundamental role in a broad range of resistive switching devices, fundamentally relying on the electrochemical metallization phenomenon. It was shown, however, that resistive switching may also appear in pure metallic nanowires lacking any silver-ion-hosting embedding environment, but this pure atomic switching mechanism fundamentally differs from the conventional electrochemical-metallization-based resistive switching. To facilitate the quantitative description of the former phenomenon, we investigate broad range of Ag atomic junctions with a special focus on the frequency-dependence and the fundamentally stochastic cycle-to-cycle variation of the switching threshold voltage. These devices are established in an ultra-high purity environment where electrochemical metallization can be excluded. The measured characteristics are successfully described by a vibrational pumping model, yielding consistent predictions for the weak frequency dependence and the large variance of the switching threshold voltage. We also demonstrate that electrochemical-metallization-based resistive switching and pure atomic switching may appear in the same device structure, and therefore the proper understanding of the pure atomic switching mechanism has a distinguished importance in silver-based electrochemical metallization cells.

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arXiv2023

Configuration-specific insight to single-molecule conductance and noise data revealed by principal component projection method

We explore the merits of neural network boosted, principal-component-projection-based, unsupervised data classification in single-molecule break junction measurements, demonstrating that this method identifies highly relevant trace classes according to the well-defined and well-visualized internal correlations of the dataset. To this end, we investigate single-molecule structures exhibiting double molecular configurations, exploring the role of the leading principal components in the identification of alternative junction evolution trajectories. We show how the proper principal component projections can be applied to separately analyze the high or low-conductance molecular configurations, which we exploit in $1/f$-type noise measurements on bipyridine molecules. This approach untangles the unclear noise evolution of the entire dataset, identifying the coupling of the aromatic ring to the electrodes through the $π$ orbitals in two distinct conductance regions, and its subsequent uncoupling as these configurations are stretched.

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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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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.

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arXiv2021

1/f noise spectroscopy and noise tailoring of nanoelectronic devices

In this paper, we review the 1/f-type noise properties of nanoelectronic devices focusing on three demonstrative platforms: resistive switching memories, graphene nanogaps and single-molecule nanowires. The functionality of such ultrasmall devices is confined to an extremely small volume, where bulk considerations on the noise loose their validity: the relative contribution of a fluctuator heavily depends on its distance from the device bottleneck, and the noise characteristics are sensitive to the nanometer-scale device geometry and the details of the mostly non-classical transport mechanism. All these are reflected by a highly system-specific dependence of the noise properties on the active device volume (and the related device resitance), the frequency, or the applied voltage. Accordingly, 1/f-type noise measurements serve as a rich fingerprint of the relevant transport and noise-generating mechanisms in the studied nanoelectronic systems. Finally, we demonstrate that not only the fundamental understanding and the targeted noise suppression is fueled by the 1/f-type noise analysis, but novel probabilistic computing hardware platforms heavily seek well tailorable nanoelectric noise sources.

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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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arXiv2020

Voltage-controlled binary conductance switching in gold-4,4'-bipyridine-gold single-molecule nanowires

We investigate gold-4,4'-bipyridine-gold single-molecule junctions with the mechanically controllable break junction technique at cryogenic temperature ($T=4.2\,\text{K}$). We observe bistable probabilistic conductance switching between the two molecular binding configurations, influenced both by the mechanical actuation, and the applied voltage. We demonstrate that the relative dominance of the two conductance states is tunable by the electrode displacement, whereas the voltage manipulation induces an exponential speedup of both switching times. The detailed investigation of the voltage-tunable switching rates provides an insight into the possible switching mechanisms.

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arXiv2016

Pulling Platinum Atomic Chains by Carbon Monoxide Molecules

The interaction of carbon monoxide molecules with atomic-scale platinum nanojunctions is investigated by low temperature mechanically controllable break junction experiments. Combining plateaus' length analysis, two dimensional conductance-displacement histograms and conditional correlation analysis a comprehensive microscopic picture is proposed about the formation and evolution of Pt-CO-Pt single-molecule configurations. Our analysis implies that before pure Pt monoatomic chains would be formed a CO molecule infiltrates the junction, first in a configuration being perpendicular to the contact axis. This molecular junction is strong enough to pull a monoatomic platinum chain with the molecule being incorporated in the chain. Along the chain formation the molecule can either stay in the perpendicular configuration, or rotate to a parallel configuration. The evolution of the single-molecule configurations along the junction displacement shows quantitative agreement with theoretical predictions, justifying the interpretation in terms of perpendicular and parallel molecular alignment. Our analysis demonstrates that the combination of two dimensional conductance-displacement histograms with conditional correlation analysis is a useful tool to separately analyze fundamentally different types of junction trajectories in single molecule break junction experiments.

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arXiv2016

Precursor configurations and post-rupture evolution of Ag-CO-Ag single-molecule junctions

Experimental correlation analysis and first-principles theory are used to probe the structure and evolution of Ag-CO-Ag single-molecule junctions, both before the formation, and after the rupture of the junctions. Two dimensional correlation histograms and conditional histograms demonstrate that prior to the single-molecule bridge configuration the CO molecule is already bound parallel to the Ag single-atom contact. This molecular precursor configuration is accompanied by the opening of additional conductance channels compared to the single-channel transport in pure Ag monoatomic junctions. To investigate the post-rupture evolution of the junction we introduce a cross-correlation analysis between the opening and the subsequent closing conductance traces. This analysis implies that the molecule is bound rigidly to the apex of one electrode, and so the same single-molecule configuration is re-established as the junction is closed. The experimental results are confirmed by ab initio simulations of the evolution of contact geometries, transmission eigenvalues and scattering wavefunctions.

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arXiv2016

Alternative types of molecule-decorated atomic chains in Au-CO-Au single-molecule junctions

We investigate the formation and evolution of Au-CO single-molecule break junctions. The conductance histogram exhibits two distinct molecular configurations, which are further investigated by a combined statistical analysis. According to conditional histogram and correlation analysis these molecular configurations show strong anticorrelations with each other and with pure Au monoatomic junctions and atomic chains. We identify molecular precursor configurations with somewhat higher conductance which are formed prior to single-molecule junctions. According to detailed length analysis two distinct types of molecule-affected chain formation processes are observed, and we compare these results to former theoretical calculations considering bridge and atop type molecular configurations where the latter has reduced conductance due to destructive Fano interference.

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