arXiv subjects

Miklós Csontos

Publications and source records attributed to Miklós Csontos.

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.

cond-mat.mes-hall↗

arXiv2026

20 ps Non-Destructive Read and 1 ns Write Operations at <5 V in Ferroelectric HfO2/ZrO2 Non-Volatile Memories

Achieving low-voltage, nanosecond multi-level programming and non-destructive read-out of ferroelectric non-volatile memories (NVM) is critical for analog in-memory computing architectures relying on ferroelectric capacitive devices (FeCap). We integrate HfO2/ZrO2 ferroelectric nanolayers concurrently in the BEOL of CMOS and on SiO2/Si, achieving nanosecond multilevel switching with programming voltages below 5 V. Partial ferroelectric switching enhances FeCap endurance above 1011 cycles, leading to MemCapacitance (MC) states with non-destructive read-out and 10-year retention. However, experiments reveal the collapse of the MC window for read frequencies above 1 MHz. To overcome this speed limit, we introduce a novel, non-destructive readout methodology. Using electrical pulses with widths down to 20 ps, below the RC time constant of the FeCaps, we enable measurement of the polarization-dependent leakage current, providing ultrafast and non-destructive read operations at only 14 fJ.

physics.app-ph↗

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.

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↗

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.

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↗

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.

cond-mat.mes-hall↗