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.

Neurodynamic behavior of artificial neuron circuits made of Mott memristors provides versatile opportunities to utilize them for artificial sensing. Their compactness and energy-efficient spike generation enable integration into medical implants. This work demonstrates 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 auditory sensing unit is realized by a piezoelectric MEMS cantilever coupled to a single VO2 nanogap Mott memristor-based oscillator. This configuration enables FFT-free, frequency-selective sensing and direct spike generation, forming a biomimetic auditory front end. The sensing unit exhibits frequency-selective detection of mechanical vibrations in the nanometer to tens-of-nanometers displacement range and generates biomimetic spiking waveforms. Spike rate-encoding of the input amplitude is demonstrated, with output spiking frequencies tunable between approximately 100 Hz and 1 kHz depending on the excitation level. The waveform is finally converted to a biphasic shape suitable for cochlear implant stimulation. Through realizing temporal spike-encoding, a fundamental principle in the healthy auditory pathway, 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 to current commercial solutions.

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.

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. 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 is severely limited, which has restricted their efficient and energy‐saving use. In this study, it is showed how to increase the oscillating frequency by more than an order of magnitude into the 100 MHz range utilizing ultrasmall, ≈30 nm wide active volume VO 2 devices and optimizing the circuit layout for high frequency operation. In addition, the physical limiting factors of the oscillation frequencies are studied by investigating the complex switching dynamics of our nanoscale VO 2 devices. These dynamical studies, together with simulations, provide a clear conclusion on the maximum achievable operating frequencies and the optimal operating parameters under which these can be reached.

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. VO2 Mott memristors are particularly promising building blocks for such oscillating neural networks. Until now, however, not only the maximum frequency of VO2 oscillating neural networks, but also the maximum frequency of individual VO2 oscillators is severely limited, which has restricted their efficient and energy-saving use. In this study, it is showed how to increase the oscillating frequency by more than an order of magnitude into the 100 MHz range utilizing ultrasmall, approximate to 30 nm wide active volume VO2 devices and optimizing the circuit layout for high frequency operation. In addition, the physical limiting factors of the oscillation frequencies are studied by investigating the complex switching dynamics of our nanoscale VO2 devices. These dynamical studies, together with simulations, provide a clear conclusion on the maximum achievable operating frequencies and the optimal operating parameters under which these can be reached.

The effect of pulse direct current (DC) reactive ion co-sputtering parameters on the morphology, crystal structure, residual stress, band gap, and piezoelectric properties of the Al(1-x)ScxN thin film deposited in large target-to-substrate distance (TSD) system has been studied using Scanning Probe Microscopy, X ray Diffractometry, Spectroscopic Ellipsometry, and profilometer, among others. The process pressure was revealed to be the key factor which essentially determines the quality of the film for such system. As low as 0.2 Pa working pressure is needed to achieve smooth nitride layer with good piezoelectric properties. High N-2/(Ar + N-2) gas ratio also was shown to result in better film properties. Residual stress in nitride film, and thereby the optical band gap can be tuned by variation of process pressure, gas ratio, and Sc fraction in the studied x range (0-0.5). The Al(1-x)ScxN film deposited at low pressure, medium N-2 gas ratio with x similar to 0.41 shows high piezoelectric coefficient, relatively low residual stress, and smooth surface. Top electrode has been applied to eliminate the interfering effect of the restraining force induced by the unexcited matrix materials around tip/sample contact for Piezoresponse Force Microscopic (PFM) measurement of piezoelectric constant of thin nitride film. We have shown by comparing the corrected d(33corr) data determined with PFM to those obtained from direct piezoelectric method that PFM using proper measurement conditions and correction can be applied as a quantitative method for study of piezoelectric properties of thin film.

Beyond-Moore computing technologies are expected to provide a sustainable alternative to the von Neumann approach not only due to their down-scaling potential but also via exploiting device-level functional complexity at the lowest possible energy consumption. The dynamics of the Mott transition in correlated electron oxides, such as vanadium dioxide, has been identified as a rich and reliable source of such functional complexity. However, its full potential in high-speed and low-power operation has been largely unexplored. We fabricated nanoscale VO2 devices embedded in a broadband test circuit to study the speed and energy limitations of their resistive switching operation. Our picosecond time-resolution, real-time resistive switching experiments and numerical simulations demonstrate that tunable low-resistance states can be set by the application of 20 ps long, <1.7 V amplitude voltage pulses at 15 ps incubation times and switching energies starting from a few femtojoule. Moreover, we demonstrate that at nanometer-scale device sizes not only the electric field induced insulator-to-metal transition but also the thermal conduction limited metal-to-insulator transition can take place at time scales of 100s of picoseconds. These orders of magnitude breakthroughs can be utilized to design high-speed and low-power dynamical circuits for a plethora of neuromorphic computing applications from pattern recognition to numerical optimization.

Összefoglalás. Napjainkra az információs technológiák fejlődése elérte azt a szintet, ahol a gyorsuló ütemben létrejövő adattömeg feldolgozásához már sok esetben elégtelenek a klasszikus, Neumann-elvek alapján működő számítógépek. A jelenség újszerű szoftveres megoldások, biológiai ihletésű algoritmusok, neurális hálózatok elterjedéséhez vezetett, ám ezek hatékony alkalmazásához teljesen új hardveres megoldások szükségesek. Jelen kézirat ilyen újszerű architektúrákhoz fejlesztett, Si-mikrochip-alapú memóriatulajdonsággal rendelkező nanoméretű áramköri elemek kísérleti eredményeit mutatja be, illetve azok egy-egy specifikus információfeldolgozási feladatra történő alkalmazhatóságával foglalkozik. Summary. Resistive switching memory devices, also known as memristors, are generally metal-insulator-metal nanostructures whose conductivity can be varied via electrical signals, enabling information storage in the value of the conductivity. Based on this property, memristive devices provide a promising platform for hardware-level encoding of large matrices. With a network of memristors, computationally intensive vector-matrix operations can be performed in a single step, considerably speeding up the operation of an artificial neural network (ANN). Memristors can also serve as real physical building blocks for biologically inspired algorithms through their neuromorphic properties. Via building simple circuits, such devices can be used to create oscillators or artificial neurons, which can be utilized for the implementation of oscillatory neural networks (ONN) or spiking neural networks (SNN). Another interesting feature of these memristive neuromorphic circuits is that they can be directly used for information processing tasks at the edge of a network. Memristors facilitate such edge computing applications which usually require energy-efficient operation and small size of the processor unit. Edge computing approaches have several advantages over centralized data processing from the aspect of security, e.g., significantly reducing time latency of sending large amounts of data to a central hub, and by processing sensitive data locally and independently of the central servers. Present work focuses on the experimental investigation of purpose-built nanoscale memristive devices, revealing their physical processes. A superconducting spectroscopy measurement technique is developed for the non-destructive detection of atomic scale memristive filaments during device operation (Török et al. 2020; Török et al. 2023). In addition, the tunable stochasticity of the nucleation process is revealed by statistical studies of the set process in silicon oxide memristors (Török et al. 2022). This finding provides a basis for the physical realization of neural activation functions, stochastically firing neurons or energy-efficient true random number generation. Finally, the applicability of the investigated nanoscale memristive devices is illustrated through two examples. The concept of a neuromorphic, memristor-based auditory sensing unit is presented, leading towards medical application in a fully implantable cochlear implant. Last, the feasibility of a hardware-level stochastic optimization procedure is introduced (Fehérvári et al. 2023), based entirely on memristive elements, utilizing tunable noise characteristics of the devices (Sánta et al. 2021).

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.

The present work explores the atomic layer deposition (ALD) of VO2 layers for resistive switching applications. Tetrakis (ethylmethylamino)vanadium (TEMAV) precursor was used combined with different oxidants, deposition temperatures, and annealing procedures, and the structural and electrical properties of the layers were analysed. All the as-deposited layers were amorphous, but an annealing in oxygen containing atmosphere at temperatures exceeding 400 degrees C yielded pure and crystalline VO2 layers. The thus prepared films are compact with crystallite sizes between 50 and 100 nm, displaying excellent electrical switching properties, with their resistivity decreasing 3 orders of magnitude at 68 degrees C.

Analog tunable memristors are widely utilized as artificial synapses in various neural network applications. However, exploiting the dynamical aspects of their conductance change to implement active neurons is still in its infancy, awaiting the realization of efficient neural signal recognition functionalities. Here we experimentally demonstrate an artificial neural information processing unit that can detect a temporal pattern in a very noisy environment, fire a single output spike upon successful detection and reset itself in a fully unsupervised, autonomous manner. This circuit relies on the dynamical operation of only two memristive blocks: a non-volatile Ta$_2$O$_5$ device and a volatile VO$_2$ unit. A fading functionality with exponentially tunable memory time constant enables adaptive operation dynamics, which can be tailored for the targeted temporal pattern recognition task. In the trained circuit false input patterns only induce short-term variations. In contrast, the desired signal activates long-term memory operation of the non-volatile component, which triggers a firing output of the volatile block.

Volatile memory devices relying on the Mott-type insulator-to-metaltransition of vanadium oxide (VO2) are widely utilizedin the field of neuromorphic computing. Such devices, however, arerealized in a nanoscale geometry, where the switching relies on theself-heating of an ultrasmall spot as well as the presence of extremelyhigh electric fields in the active region. In this paper, we investigatethe interplay of such nanoscale thermal and nonlinear electronic phenomenaby investigating the temperature and voltage dependent conductionproperties of our custom-designed VO2 devices, where aV-shaped electrode focuses the switching to an ultrasmall single-spotactive region. This simplified spatial structure of the active volumefacilitates the device modeling and the identification of physicalmechanisms behind the phase transition. We find that purely thermalor electronic effects fail to describe the device operation, however,according to our finite element simulations, a combined electronicand thermal model provides a precise description of the device characteristics.These results facilitate the understanding as well as the thermaland electronic design of novel VO2-based neuronal devices.

Resistive switching memory devices hold extensive possibilities for realizing artificial neural networks along with nonconventional computing paradigms. Studying and understanding phenomena arising at single resistive switching elements is necessary for utilizing their particular traits for computation. Tuning the variability of the set time-the timespan before the onset of the transition from a high-resistance OFF state to a low-resistance ON state-is key for making use of the inherently stochastic nature of the resistance switching effect. Here, we study the set time statistics in nanometer-sized graphene-SiOx-graphene resistive switching memory devices. For dedicated OFF state configurations, we demonstrate a universal variance of the logarithmic set time values, which is characteristic to a nucleation-driven crystallization process. Furthermore, we observe clear correlation between the OFF state resistance and the set time, and hence we explore the tunability of the set time statistics via changing the reset amplitude parameter in sequential pulsed measurements. The latter phenomenon could prove useful for controlling stochasticity in memristor-based probabilistic computing applications via the control of the active volume's nanostructure.

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.

In this study, the possibilities of noise tailoring in filamentary resistive switching memory devices are investigated. To this end, the resistance and frequency scaling of the low-frequency 1/f-type noise properties are studied in representative mainstream material systems. It is shown that the overall noise floor is tailorable by the proper material choice, as demonstrated by the order-of-magnitude smaller noise levels in Ta2O5 and Nb2O5 transition-metal oxide memristors compared to Ag-based devices. Furthermore, the variation of the resistance states allows orders-of-magnitude tuning of the relative noise level in all of these material systems. This behavior is analyzed in the framework of a point-contact noise model highlighting the possibility for the disorder-induced suppression of the noise contribution arising from remote fluctuators. These findings promote the design of multipurpose resistive switching units, which can simultaneously serve as analog-tunable memory elements and tunable noise sources in probabilistic computing machines.

Graphene nanogaps are considered as essential building blocks of two-dimensional electronic circuits, as they offer the possibility to interconnect a broad range of atomic-scale objects. Here we provide an insight into the microscopic processes taking place during the formation of graphene nanogaps through the detailed analysis of their low-frequency noise properties. Following the evolution of the noise level, we identify the fundamentally different regimes throughout the nanogap formation. By modeling the resistance and bias dependence of the noise, we resolve the major noise-generating processes: atomic-scale junction-width fluctuations in the nanojunction regime and sub-atomic gap-size fluctuations in the nanogap regime. As a milestone toward graphene-based atomic electronics, our results facilitate the automation of an optimized electrical breakdown protocol for high-yield graphene nanogap fabrication.

Due to its remarkable switching effect in electrical and optical properties, VO2 is a promising material for several applications. However, the stoichiometry control of multivalent vanadium oxides, especially with a rational deposition technique, is still challenging. Here, we propose and optimize a simple fabrication method for VO2 rich layers by the oxidation of metallic vanadium in atmospheric air. It was shown that a sufficiently broad annealing time window of 3.0–3.5 h can be obtained at an optimal oxidation temperature of 400 °C. The presence of VO2 was detected by selected area diffraction in a transmission electron microscope. According to the temperature dependent electrical measurements, the resistance contrast (R30 °C/R100 °C) varied between 44 and 68, whereas the optical switching was confirmed using in situ spectroscopic ellipsometric measurement by monitoring the complex refractive indices. The obtained phase transition temperature, both for the electrical resistance and for the ellipsometric angles, was found to be 49 ± 7 °C, i.e., significantly lower than that of the bulk VO2 of 68 ± 6 °C.

Electrochemically active metals offer advanced functionalities with respect to the well-established gold electrode arrangements in various electronic transport experiments on atomic scale objects. Such functionalities can arise from stronger interactions with the leads which provide better coupling to specific molecules and may also facilitate metallic filament formation in atomic switches. However, the higher reactivity of the electrode metal also imposes challenges in the fabrication and reliability of nanometer scale platforms, limiting the number of reported applications. Here we present a high-yield lithographic fabrication procedure suitable to extend the experimental toolkit with mechanically controllable break junctions of oxygen sensitive metallic electrodes. We fabricate and characterize silver break junctions exhibiting single-atomic conductance and superior mechanical and electrical stability at room temperature. As a proof-of-principle application, we demonstrate resistive switching between metastable few-atom configurations at finite voltage bias.