Noise Tailoring in Memristive Filaments.

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.

Unsupervised feature recognition in single-molecule break junction data.

Single-molecule break junction measurements deliver a huge number of conductance vs. electrode separation traces. During such measurements, the target molecules may bind to the electrodes in different geometries, and the evolution and rupture of the single-molecule junction may also follow distinct trajectories. The unraveling of the various typical trace classes is a prerequisite to the proper physical interpretation of the data. Here we exploit the efficient feature recognition properties of neural networks to automatically find the relevant trace classes. To eliminate the need for manually labeled training data we apply a combined method, which automatically selects training traces according to the extreme values of principal component projections or some auxiliary measured quantities. Then the network captures the features of these characteristic traces and generalizes its inference to the entire dataset. The use of a simple neural network structure also enables a direct insight into the decision-making mechanism. We demonstrate that this combined machine learning method is efficient in the unsupervised recognition of unobvious, but highly relevant trace classes within low and room temperature gold-4,4' bipyridine-gold single-molecule break junction data.

Nanosecond resistive switching in Ag/AgI/PtIr nanojunctions.

Nanometer-scale resistive switching devices operated in the metallic conductance regime offer ultimately scalable and widely reconfigurable hardware elements for novel in-memory and neuromorphic computing architectures. Moreover, they exhibit high operation speed at low power arising from the ease of the electric-field-driven redistribution of only a small amount of highly mobile ionic species upon resistive switching. We investigate the memristive behavior of a so-far less explored representative of this class, the Ag/AgI material system in a point contact arrangement established by the conducting PtIr tip of a scanning probe microscope. We demonstrate stable resistive switching duty cycles and investigate the dynamical aspects of non-volatile operation in detail. The high-speed switching capabilities are explored by a custom-designed microwave setup that enables time-resolved studies of subsequent set and reset transitions upon biasing the Ag/AgI/PtIr nanojunctions with sub-nanosecond voltage pulses. Our results demonstrate the potential of Ag-based filamentary memristive nanodevices to serve as the hardware elements in high-speed neuromorphic circuits.

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. However, 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 Nb2O5 resistive switching junctions provide profound experimental evidence that the onset of the high conductance ON state is manifested via the formation of truly atomic-sized metallic filaments.

A non-oxidizing fabrication method for lithographic break junctions of sensitive metals.

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.

Universal 1/f type current noise of Ag filaments in redox-based memristive nanojunctions.

The microscopic origins and technological impact of 1/f type current fluctuations in Ag based, filamentary type resistive switching devices have been investigated upon downscaling toward the ultimate single atomic limit. The analysis of the low-frequency current noise spectra revealed that the main electronic noise contribution arises from the resistance fluctuations due to internal dynamical defects of Ag nanofilaments. The resulting 0.01-1% current noise ratio, i.e. the total noise level with respect to the mean value of the current, is found to be universal: its magnitude only depends on the total resistance of the device, irrespective of the materials aspects of the surrounding solid electrolyte and of the specific filament formation procedure. Moreover, the resistance dependence of the current noise ratio also displays the diffusive to ballistic crossover, confirming that stable resistive switching operation utilizing Ag nanofilaments is not compromised even in truly atomic scale junctions by technologically impeding noise levels.

In situ impedance matching in Nb/Nb2O5/PtIr memristive nanojunctions for ultra-fast neuromorphic operation.

The dynamical aspects of bipolar resistive switchings have been investigated in Nb/Nb2O5/PtIr nanojunctions. We found that the widely tuneable ON and OFF state resistances are well separated at low bias. On the other hand, the high-bias regime of the resistive switchings coincides with the onset of a high nonlinearity in the current-voltage characteristics, where the impedance of both states rapidly decreases and becomes equivalent around 50 Ω. This phenomenon enables the overriding of the RC limitations of fast switchings between higher resistance ON and OFF states. Consequently, nanosecond switching times between multiple resistance states due to subnanosecond voltage pulses are demonstrated. Moreover, this finding provides the possibility of impedance engineering by the appropriate choice of voltage signals, which facilitates that both the set and reset transitions take place in an impedance matched manner to the surrounding circuit, demonstrating the merits of ultra-fast operation of Nb2O5 based neuromorphic networks.

Classification of conductance traces with recurrent neural networks.

We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.

Electronic and mechanical characteristics of stacked dimer molecular junctions.

Break-junction measurements are typically aimed at characterizing electronic properties of single molecules bound between two metal electrodes. Although these measurements have provided structure-function relationships for such devices, there is little work that studies the impact of molecule-molecule interactions on junction characteristics. Here, we use a scanning tunneling microscope based break-junction technique to study pi-stacked dimer junctions formed with two amine-terminated conjugated molecules. We show that the conductance, force and flicker noise of such dimers differ dramatically when compared with the corresponding monomer junctions and discuss the implications of these results on intra- and inter-molecular charge transport.

From electroburning to sublimation: substrate and environmental effects in the electrical breakdown process of monolayer graphene.

We report on the characterization of the electrical breakdown (EB) process for the formation of tunneling nanogaps in single-layer graphene. In particular, we investigated the role of oxygen in the breakdown process by varying the environmental conditions (vacuum and ambient conditions). We show that the density of oxygen molecules in the chamber is a crucial parameter that defines the physical breakdown process: at low density, the graphene lattice is sublimating, whereas at high density, the process involved is oxidation, independent of the substrate material. To estimate the activation energies of the two processes, we use a scheme which consists of applying voltage pulses across the junction during the breakdown. By systematically varying the voltage pulse length, and estimating the junction temperature from a 1D thermal model, we extract activation energies which are consistent with the sublimation of graphene under high vacuum and the electroburning process under air. Our study demonstrates that, in our system, a better control of the gap formation is achieved in the sublimation regime.

Multiple Physical Time Scales and Dead Time Rule in Few-Nanometers Sized Graphene-SiOx-Graphene Memristors.

The resistive switching behavior in SiOx-based phase change memory devices confined by few nanometer wide graphene nanogaps is investigated. Our experiments and analysis reveal that the switching dynamics is not only determined by the commonly observed bias voltage dependent set and reset times. We demonstrate that an internal time scale, the dead time, plays a fundamental role in the system's response to various driving signals. We associate the switching behavior with the formation of microscopically distinct SiOx amorphous and crystalline phases between the graphene electrodes. The reset transition is attributed to an amorphization process due to a voltage driven self-heating; it can be triggered at any time by appropriate voltage levels. In contrast, the formation of the crystalline ON state is conditional and only occurs after the completion of a thermally assisted structural rearrangement of the as-quenched OFF state which takes place within the dead time after a reset operation. Our results demonstrate the technological relevance of the dead time rule which enables a zero bias access of both the low and high resistance states of a phase change memory device by unipolar voltage pulses.

Asymmetry-induced resistive switching in Ag-Ag2S-Ag memristors enabling a simplified atomic-scale memory design.

Prevailing models of resistive switching arising from electrochemical formation of conducting filaments across solid state ionic conductors commonly attribute the observed polarity of the voltage-biased switching to the sequence of the active and inert electrodes confining the resistive switching memory cell. Here we demonstrate stable switching behaviour in metallic Ag-Ag2S-Ag nanojunctions at room temperature exhibiting similar characteristics. Our experimental results and numerical simulations reveal that the polarity of the switchings is solely determined by the geometrical asymmetry of the electrode surfaces. By the lithographical design of a proof of principle device we demonstrate the merits of simplified fabrication of atomic-scale, robust planar Ag2S memory cells.

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.

Resistive switching in metallic Ag2S memristors due to a local overheating induced phase transition.

Resistive switchings in nanometer-scale metallic junctions formed between an inert metallic tip and an Ag film covered by a thin Ag2S layer are investigated as a function of temperature at different biasing conditions. The observed switching threshold voltages along with the ON and OFF state resistances are quantitatively understood by taking the local overheating of the junction volume and the resulting structural phase transition of the Ag2S matrix into account. Our results demonstrate that the essential characteristics of the resistive switching in Ag2S based nanojunctions can be routinely optimized by suitable sample preparation and biasing schemes.

Non-exponential resistive switching in Ag2S memristors: a key to nanometer-scale non-volatile memory devices.

The dynamics of resistive switchings in nanometer-scale metallic junctions formed between an inert metallic tip and an Ag film covered by a thin Ag2S layer are investigated. Our thorough experimental analysis and numerical simulations revealed that the resistance change upon a switching bias voltage pulse exhibits a strongly non-exponential behaviour yielding markedly different response times at different bias levels. Our results demonstrate the merits of Ag2S nanojunctions as nanometer-scale non-volatile memory cells with stable switching ratios, high endurance as well as fast response to write/erase, and an outstanding stability against read operations at technologically optimal bias and current levels.

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.

High-yield fabrication of nm-size gaps in monolayer CVD graphene.

Herein we demonstrate the controlled and reproducible fabrication of sub-5 nm wide gaps in single-layer graphene electrodes. The process is implemented for graphene grown via chemical vapor deposition using an electroburning process at room temperature and in vacuum. A yield of over 95% for the gap formation is obtained. This approach allows producing single-layer graphene electrodes for molecular electronics at a large scale. Additionally, from Raman spectroscopy and electroburning carried out simultaneously, we can follow the heating process and infer the temperature at which the gap formation happens.

A fast operation of nanometer-scale metallic memristors: highly transparent conductance channels in Ag2S devices.

The nonlinear transport properties of nanometer-scale junctions formed between an inert metallic tip and an Ag film covered by a thin Ag2S layer are investigated. Suitably prepared samples exhibit memristive behavior with technologically optimal ON and OFF state resistances yielding to resistive switching on the nanosecond time scale. Utilizing point contact Andreev reflection spectroscopy, we studied the nature of electron transport in the active volume of memristive junctions showing that both the ON and OFF states correspond to truly nanometer-scale, highly transparent metallic channels. Our results demonstrate the merits of Ag2S nanojunctions as nanometer-scale memory cells which can be switched by nanosecond voltage pulses.

Correlating structure, conductance, and mechanics of silver atomic-scale contacts.

We measure simultaneously force and conductance of Ag metal point-contacts under ambient conditions at room temperature. We observe the formation of contacts with a conductance close to 1 G0, the quantum of conductance, which can be attributed to a single-atom contact, similar to those formed by Au. We also find two additional conductance features at ∼0.4 G0 and ∼1.3 G0, which have been previously ascribed to contacts with oxygen contaminations. Here, using a conductance cross-correlation technique, we distinguish three different atomic-scale structural motifs and analyze their rupture forces and stiffness. Our results allow us to assign the ∼0.4 G0 conductance feature to an Ag-O-Ag contact and the ∼1.3 G0 feature to an Ag-Ag single-atom contact with an oxygen atom in parallel. Utilizing complementary information from force and conductance, we thus demonstrate the correlation of conductance with the structural evolution at the atomic scale.

Correlation analysis of atomic and single-molecule junction conductance.

The break-junction technique is widely used to measure electronic properties of nanoscale junctions including metal point-contacts and single-molecule junctions. In these measurements, conductance is measured as a function of electrode displacement yielding data that is analyzed by constructing conductance histograms to determine the most frequently observed conductance values in the nanoscale junctions. However much of the rich physics in these measurements is lost in this simple analysis technique. Conductance histograms cannot be used to study the statistical relation of distinct junction configurations, to distinguish structurally different configurations that have similar conductance values, or to obtain information on the relation between conductance and junction elongation. Here, we give a detailed introduction to a novel statistical analysis method based on the two-dimensional cross-correlation histogram (2DCH) analysis of conductance traces and show that this method provides new information about the relation of different junction configurations that occur during the formation and evolution of metal and single-molecule junctions. We first illustrate the different types of correlation effects by using simulated conductance traces. We then apply this analysis method to several different experimental examples. We show from break-junction measurements of different metal point-contacts that in aluminum, the first conductance histogram peak corresponds to two different junction structures. In tantalum, we identify the frequent absence of adhesive instability. We show that conductance plateaus shift in a correlated manner in iron and vanadium junctions. Finally, we highlight the applicability of the correlation analysis to single-molecule platinum-CO-platinum and gold-4,4'-bipyridine-gold junctions.