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Performances of bipolar Au/NbOx/Nb devices were investigated by correlating the material properties of electrochemically grown NbOx with resistive switching functionalities.

Self-organizing memristive nanowire connectomes have been exploited for physical (in materia) implementation of brain-inspired computing paradigms. Despite the emergent behavior was shown to rely on weight plasticity at single junction/synapse level and wiring plasticity involving topological changes, a shift to multiterminal paradigms is needed to unveil dynamics at the network level. Here, we report on tomographical evidence of memory engrams(or memory traces) in nanowire connectomes, i.e., chemical and physical changes in biological neural substrates supposed to endow the representation of experience stored in the brain. An experimental/modeling approach shows that spatially correlated short-term plasticity effects can turn into long-lasting engram memory patterns inherently related to network topology inhomogeneities. The ability to exploit both encoding and consolidation of information on the same physical substrate would open radically new perspectives for in materiacomputing, while offering to neuroscientists an alternative platform to understand the role of memory in learning and knowledge.

ACS applied materials & interfaces 12(43), 48773-48780 (2020). doi:10.1021/acsami.0c13020, Published by American Chemical Society, Washington, DC

Physical reservoir computing (RC) represents a computational framework that exploits information-processing capabilities of programmable matter, allowing the realization of energy-efficient neuromorphic hardware with fast learning and low training cost. Despite self-organized memristive networks have been demonstrated as physical reservoir able to extract relevant features from spatiotemporal input signals, multiterminal nanonetworks open the possibility for novel strategies of computing implementation. In this work, we report on implementation strategies of in materia RC with self-assembled memristive networks. Besides showing the spatiotemporal information processing capabilities of self-organized nanowire networks, we show through simulations that the emergent collective dynamics allows unconventional implementations of RC where the same electrodes can be used as both reservoir inputs and outputs. By comparing different implementation strategies on a digit recognition task, simulations show that the unconventional implementation allows a reduction of the hardware complexity without limiting computing capabilities, thus providing new insights for taking full advantage of in materia computing toward a rational design of neuromorphic systems.

Memristive devices relying on redox-based resistive switching mechanisms represent promising candidates for the development of novel computing paradigms beyond von Neumann architecture. Recent advancements in understanding physicochemical phenomena underlying resistive switching have shed new light on the importance of an appropriate selection of material properties required to optimize the performance of devices. However, despite great attention has been devoted to unveiling the role of doping concentration, impurity type, adsorbed moisture, and catalytic activity at the interfaces, specific studies concerning the effect of the counter electrode in regulating the electronic flow in memristive cells are scarce. In this work, the influence of the metal-insulator Schottky interfaces in electrochemical metallization memory (ECM) memristive cell model systems based on single-crystalline ZnO nanowires (NWs) is investigated following a combined experimental and modeling approach. By comparing and simulating the electrical characteristics of single NW devices with different contact configurations and by considering Ag and Pt electrodes as representative of electrochemically active and inert electrodes, respectively, we highlight the importance of an appropriate choice of electrode materials by taking into account the Schottky barrier height and interface chemistry at the metal-insulator interfaces. In particular, we show that a clever choice of metal-insulator interfaces allows to reshape the hysteretic conduction characteristics of the device and to increase the device performance by tuning its resistance window. These results obtained from single NW-based devices provide new insights into the selection criteria for materials and interfaces in connection with the design of advanced ECM cells.

Hardware implementation of neural networks represents a milestone for exploiting the advantages of neuromorphic-type data processing and for making use of the inherent parallelism associated with such structures. In this context, memristive devices with their analogue functionalities are called to be promising building blocks for the hardware realization of artificial neural networks. As an alternative to conventional crossbar architectures where memristive devices are organized with a top-down approach in a grid-like fashion, neuromorphic-type data processing and computing capabilities have been explored in networks realized according to the principle of self-organization similarity found in biological neural networks. Here, we explore structural and functional connectivity of self-organized memristive nanowire (NW) networks within the theoretical framework of graph theory. While graph metrics reveal the link of the graph theoretical approach with geometrical considerations, results show that the interplay between network structure and its capacity to transmit information is related to a phase transition process consistent with percolation theory. Also the concept of memristive distance is introduced to investigate activation patterns and the dynamic evolution of the information flow across the network represented as a memristive graph. In agreement with experimental results, the emergent short-term dynamics reveals the formation of self-selected pathways with enhanced transport characteristics connecting stimulated areas and regulating the trafficking of the information flow. The network capability to process spatio-temporal input signals can be exploited for the implementation of unconventional computing paradigms in memristive graphs that take into advantage the inherent relationship between structure and functionality as in biological systems.

Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.

Advanced materials interfaces 10(16), 2100915 (2021). doi:10.1002/admi.202100915, Published by Wiley-VCH, Weinheim

The knowledge of the spatial distribution of the electrical conductivity of metallic nanowire networks (NWN) is important for tailoring the performance in applications. This work focuses on Electrical Resistance Tomography (ERT), a technique that maps the electrical conductivity of a sample from several resistance measurements performed on its border. We show that ERT can be successfully employed for NWN characterisation if a dedicated measurement protocol is employed. When applied to other materials, ERT measurements are typically performed with a constant current excitation; we show that, because of the peculiar microscopic structure and behaviour of metallic NWN, a constant voltage excitation protocols is preferable. This protocol maximises the signal to noise ratio in the resistance measurements—and thus the accuracy of ERT maps—while preventing the onset of sample alterations.

Memristive devices based on a resistive switching mechanism are considered very promising for nonvolatile memory and unconventional computing applications, even though many details of the switching mechanisms are not yet fully understood. Here, we report a nanostructural study by means of high-resolution transmission electron microscopy and spectroscopy techniques of a Ag/ZnO/Pt memristive device. To ease the localization of the filament position for its characterization, we propose to use the guiding effect of regular perturbation arrays obtained by FIB technology to assist the filament formation. HRTEM and EDX were used to identify the composition and crystalline structure of the so-obtained conductive filaments and surrounding regions. It was determined that the conducting paths are composed mainly of monocrystalline Ag, which remains polycrystalline in some circumstances, including the zone where the switching occurs and at secondary filaments created at the grain boundaries of the polycrystalline ZnO matrix. We also observed that the ZnO matrix shows a degraded quality in the switching zone, while it remains unaltered in the rest of the memristive device.

Memristive and resistive switching devices are considered promising building blocks for the realization of artificial neural networks and neuromorphic systems. Besides conventional top-down memristive devices based on thin films, resistive switching devices based on nanowires (NWs) have attracted great attention, not only for the possibility of going beyond current scaling limitations of the top-down approach, but also as model systems for the localization and investigation of the physical mechanism of switching. This work reports on the fabrication of memristive devices based on ZnO NWs, from NW synthesis to single NW-based memristive cell fabrication and characterization. The bottom-up synthesis of ZnO NWs was performed by low-pressure chemical vapor deposition according to a self-seeding vapor-solid (VS) mechanism on a Pt substrate over large scale (∼cm2), without the requirement of previous seed deposition. The grown ZnO NWs are single crystalline with wurtzite crystal structure and are vertically aligned respect to the growth substrate. Single NWs were then contacted by means of asymmetric contacts, with an electrochemically active and an electrochemically inert electrode, to form NW-based electrochemical metallization memory cells that show reproducible resistive switching behaviour and neuromorphic functionalities including short-term synaptic plasticity and paired pulse facilitation. Besides representing building blocks for NW-based memristive and neuromorphic systems, these single crystalline devices can be exploited as model systems to study physicochemical processing underlaying memristive functionalities thanks to the high localization of switching events on the ZnO crystalline surface.

The physical mechanism involved in resistive switching phenomena occurring in devices based on ZnO nanowire (NW) arrays may vary considerably, also depending on the structure of the switching layer. In particular, it is shown here that the formation of a ZnO base layer between the metallic catalyst substrate and the NW, which is typical of CVD-grown ZnO NW arrays, should not be neglected when explaining the switching physical mechanism. The structural and electrical properties of this layer are investigated after the mechanical removal of NWs. Electrical measurements were performed in the presence of NWs and, after their removal, showed that the base alone exhibits resistive switching properties. The proposed switching mechanism is based on the creation/rupture of an oxygen vacancies conductive path along grain boundaries of the polycrystalline base. The creation of the filament is facilitated by the high concentration of vacancies at the grain boundaries that are oriented perpendicularly to the electrode...

Resistive switching (RS) devices based on self-assembled nanowires (NWs) and nanorods (NRs) represent a fascinating alternative to conventional devices with thin film structure. The high surface-to-volume ratio may indeed provide the possibility of modulating their functionalities through surface effects. However, devices based on NWs usually suffer from low resistive switching performances in terms of operating voltages, endurance and retention capabilities. In this work, we report on the resistive switching behaviour of ZnO NW arrays, grown by hydrothermal synthesis, that exhibit stable, bipolar resistive switching characterized by SET/RESET voltages lower than 3 V, endurance higher than 1100 cycles and resistance state retention of more than 105 s. The physical mechanism underlying these RS performances can be ascribed to nanoionic processes involving the formation/rupture of conductive paths assisted by oxygen-related species in the ZnO active layer. The reported results represent, to the best of our knowledge, the best resistive switching performances observed in ZnO NW arrays in terms of endurance and retention.

This letter deals with short-term plasticity (STP) effects in the conduction characteristics of single crystalline ZnO nanowires, including potentiation, depression and relaxation phenomena. The electrical behavior of the structures is modeled following Chua's approach for memristive systems, i.e. one equation for the electron transport and one equation for the memory state of the device. Linear conduction is assumed in the first case together with a voltage-controlled rate balance equation for the normalized conductance. The devices are subject to electrical stimuli such as ramped and pulsed voltages of both polarities with varying amplitude and frequency. In each case, the proposed model is able to account for the STP effects exhibited by ZnO highlighting its neuromorphic capabilities for bio-inspired circuits. An equivalent circuit representation and the SPICE implementation of the compact model is also provided.

This letter deals with the compact modeling of the I-V characteristics of single crystalline ZnO nanowires (NW) attached to two metal electrodes (Pt and Ag). Starting from the standard model of electron transport in these structures based on the series combination of two back-to-back Schottky diodes, three different approaches which account for the role played by the NW series resistance are presented. The first approach considers a fixed potential drop across the NW, the second one involves the solution to the problem of a diode with linear and nonlinear series resistances using the Lambert W function, and the third one consists in a behavioral model with continuous first derivative suitable for circuit simulation environments. In the three cases, the proposed solutions are consistent with the observed electrical constraints both at low (linear I-V relationship) and high (current saturation) voltages.

In this work, we report on the metal–insulator transition and electronic transport properties of single crystalline ZnO nanowires synthetized by means of Chemical Vapor Deposition. After evaluating the effect of adsorbed species on transport properties, the thermally activated conduction mechanism was investigated by temperature-dependent measurements in the range 81.7–250 K revealing that the electronic transport mechanism in these nanostructures is in good agreement with the presence of two thermally activated conduction channels. More importantly, it was observed that the electrical properties of ZnO NWs can be tuned from semiconducting to metallic-like as a function of temperature with a metal-to-insulator transition (MIT) observed at a critical temperature above room temperature (T c ∼ 365 K). Charge density and mobility were investigated by means of field effect measurements in NW field-effect transistor configuration. Results evidenced that the peculiar electronic transport properties of ZnO NWs are related to the high intrinsic n-type doping of these nanostructures that is responsible, at room temperature, of a charge carrier density that lays just below the critical concentration for the MIT. This work shows that native defects, Coulomb interactions and surface states influenced by adsorbed species can significantly influence charge transport in NWs.

Acting as artificial synapses, two‐terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Current memristive crossbar architectures demonstrate the implementation of neuromorphic computing paradigms, although they are unable to emulate typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Herein, self‐organizing memristive random nanowire (NW) networks with functional connectivity able to display homo‐ and heterosynaptic plasticity is reported thanks to the mutual electrochemical interaction among memristive NWs and NW junctions. In particular, it is shown that rewiring and reweighting effects observed in single NWs and single NW junctions, respectively, are responsible for structural plasticity of the network under electrical stimulation. Such biologically inspired systems allow a low‐cost realization of neural networks that can learn and adapt when subjected to multiple external stimuli, emulating the experience‐dependent synaptic plasticity that shape the connectivity and functionalities of the nervous system that can be exploited for hardware implementation of unconventional computing paradigms.

Electrochemical metallization ReRAMs (ECM) - Experiments and modelling: General discussion

Metal-semiconductor interfaces play a crucial role not only for regulating the electronic conduction mechanism but also in determining new functionalities in nanosized devices. In this work, we reported the investigation of the junction properties of single ZnO nanowires (NWs) asymmetrically contacted by means of a Pt electrochemically inert and a Cu electrochemically active electrode. At low applied voltages, these devices operate as diodes where the conduction mechanism was found to be dominated by the Schottky barrier at the Cu/ZnO interface. Junction parameters such as the Schottky barrier height, the ideality factor and the series resistance have been analyzed according to the thermionic emission theory. Different methods for parameter retrieval from I-V-T measurements are discussed and compared. A potential fluctuation model is considered in order to account for barrier inhomogeneities, revealing the presence of two Gaussian distribution of barrier heights. On the other hand, new device features arise from electrochemical dissolution and migration of Cu ions along the NW when high electric fields are implied. These electrochemical processes are underlaying the resistive switching and memristive behavior observed in single ZnO NWs, as suggested also by direct observation of Cu nanoclusters along the nanostructures after the switching events.

This work reports the fabrication of memristive devices based on iron oxide (Fe2O3) thin films grown by atomic layer deposition (ALD) using ferrocene as iron precursor and ozone as oxidant. An excellent control of the ALD process was achieved by using an experimental procedure based on a sequence of micro-pulses, which provided long residence time and homogeneous diffusion of precursors, allowing ALD of thin films with smooth morphology and crystallinity which was found to increase with layer thickness, at temperatures as low as 250 °C. The resistive switching of symmetric Pt/Fe2O3/Pt thin film devices exhibited bipolar mode with good stability and endurance. Multi-level switching was achieved via current and voltage control. It was proved that the ON state regime can be tuned by changing the current compliance while the OFF state can be changed to intermediate levels by decreasing the maximum voltage during RESET. The structural analysis of the switched oxide layer revealed the presence of nano-sized crystalline domains corresponding to different iron oxide phases, suggesting that Joule heating effects during I-V cycling are responsible for a crystallization process of the pristine amorphous layer.

The effect of laser irradiation on chemically vapor deposited (CVD) graphene was studied by analyzing the temporal evolution of Raman spectra acquired under various illumination conditions. The spectra showed that the normalized intensity of the defect-related peak increases with the square root of the exposure time and varies almost linearly with the laser power density. Furthermore, the hardness of graphene to radiation damage depends on its intrinsic structural quality. The results suggest that, contrary to the common belief, micro-Raman spectroscopy cannot be considered a noninvasive tool for the characterization of graphene. The experimental observations are compatible with a model that we derived from the interpretative approach of the Staebler–Wronski effect in hydrogenated amorphous silicon; this approach assumes that the recombination of photoexcited carriers induces the breaking of weak C–C bonds.

Surface and quantum confinement effects in one-dimensional systems such as ZnO nanowires are responsible for novel electrical properties, and can be exploited to tune electrical transport on the nanoscale. The investigation of new physical mechanisms for resistive switching can be fulfilled by studying metal/insulator/metal memristive devices that take advantage of the unique properties of one-dimensional nanoscale metal oxides. In particular, the mechanisms of resistive switching between multiple resistance states in such nanostructures can be associated with the variation of internal physical states. Here we demonstrate both experimentally and theoretically that core–shell structures based on polyacrylic acid coated ZnO nanowires exhibit a resistive switching behavior characterized by internal multiple resistance states, owing to the changes in surface states induced by redox reactions occurring at their surfaces. The introduction of a thin layer of polymer coating resulted in a resistive switching between more than two states. Specifically, the existence of two intermediate states in addition to the high and low resistance states was revealed during DC measurements in voltage sweep mode. All resistive states showed low variability over cycling. The mechanism of switching between multiple steps, as probed by density functional theory calculations, was associated with redox reactions involving species at the interface (e.g. methanal or hydroxyl groups), each characterized by a given redox potential. Therefore, multiple resistance states were induced by specific and stable threshold voltages, as shown experimentally.

Resistive switching (RS) devices are considered as the most promising alternative to conventional random access memories. They interestingly offer effective properties in terms of device scalability, low power-consumption, fast read/write operations, high endurance and state retention. Moreover, neuromorphic circuits and synapse-like devices are envisaged with RS modeled as memristors, opening the route toward beyond-Von Neumann computing architectures and intelligent systems. This work investigates how the RS properties of zinc oxide thin films are related to both sputtering deposition process and device configuration, i.e. valence change memory and electrochemical metallization memory (ECM). Different devices, with an oxide thickness ranging from 50-250 nm, are fabricated and deeply characterized. The electrical characterization evidences that, differently from typical nanoscale amorphous oxides employed for resistive RAMs (HfO x , WO x , etc), sub-micrometric thicknesses of polycrystalline ZnO layers with ECM configuration are needed to achieve the most reliable devices. The obtained results are deeply discussed, correlating the RS mechanism to material nanostructure.

In this paper we report on a systematic study of Cu thin film dewetting by the monitoring of the intensity of the infra-red emission from the film surface during Rapid Thermal Chemical Vapor Deposition of graphene. The time evolution of Cu coverage highlights three typical stages of dewetting which strongly depend not only on the temperature and film thickness, but also on the pressure and composition of the gas in chamber. Consequently, we demonstrate that the Cu surface can be effectively activated in films at temperatures lower than in foils and the process can be fully controlled by adjusting those parameters, in order to reach the optimal conditions for graphene growth.