Electrochemical rewiring through quantum conductance effects in single metallic memristive nanowires.

Memristive devices have been demonstrated to exhibit quantum conductance effects at room temperature. In these devices, a detailed understanding of the relationship between electrochemical processes and ionic dynamic underlying the formation of atomic-sized conductive filaments and corresponding electronic transport properties in the quantum regime still represents a challenge. In this work, we report on quantum conductance effects in single memristive Ag nanowires (NWs) through a combined experimental and simulation approach that combines advanced classical molecular dynamics (MD) algorithms and quantum transport simulations (DFT). This approach provides new insights on quantum conductance effects in memristive devices by unravelling the intrinsic relationship between electronic transport and atomic dynamic reconfiguration of the nanofilment, by shedding light on deviations from integer multiples of the fundamental quantum of conductance depending on peculiar dynamic trajectories of nanofilament reconfiguration and on conductance fluctuations relying on atomic rearrangement due to thermal fluctuations.

Electrical and Thermal Conductivities of Single CuxO Nanowires.

Copper oxide nanowires (NWs) are promising elements for the realization of a wide range of devices for low-power electronics, gas sensors, and energy storage applications, due to their high aspect ratio, low environmental impact, and cost-effective manufacturing. Here, we report on the electrical and thermal properties of copper oxide NWs synthetized through thermal growth directly on copper foil. Structural characterization revealed that the growth process resulted in the formation of vertically aligned NWs on the Cu growth substrate, while the investigation of chemical composition revealed that the NWs were composed of CuO rather than Cu2O. The electrical characterization of single-NW-based devices, in which single NWs were contacted by Cu electrodes, revealed that the NWs were characterized by a conductivity of 7.6 × 10-2 S∙cm-1. The effect of the metal-insulator interface at the NW-electrode contact was analyzed by comparing characterizations in two-terminal and four-terminal configurations. The effective thermal conductivity of single CuO NWs placed on a substrate was measured using Scanning Thermal Microscopy (SThM), providing a value of 2.6 W∙m-1∙K-1, and using a simple Finite Difference model, an estimate for the thermal conductivity of the nanowire itself was obtained as 3.1 W∙m-1∙K-1. By shedding new light on the electrical and thermal properties of single CuO NWs, these results can be exploited for the rational design of a wide range of optoelectronic devices based on NWs.

Effect of electrode materials on resistive switching behaviour of NbOx-based memristive devices.

Memristive devices that rely on redox-based resistive switching mechanism have attracted great attention for the development of next-generation memory and computing architectures. However, a detailed understanding of the relationship between involved materials, interfaces, and device functionalities still represents a challenge. In this work, we analyse the effect of electrode metals on resistive switching functionalities of NbOx-based memristive cells. For this purpose, the effect of Au, Pt, Ir, TiN, and Nb top electrodes was investigated in devices based on amorphous NbOx grown by anodic oxidation on a Nb substrate exploited also as counter electrode. It is shown that the choice of the metal electrode regulates electronic transport properties of metal-insulator interfaces, strongly influences the electroforming process, and the following resistive switching characteristics. Results show that the electronic blocking character of Schottky interfaces provided by Au and Pt metal electrodes results in better resistive switching performances. It is shown that Pt represents the best choice for the realization of memristive cells when the NbOx thickness is reduced, making possible the realization of memristive cells characterised by low variability in operating voltages, resistance states and with low device-to-device variability. These results can provide new insights towards a rational design of redox-based memristive cells.

Tomography of memory engrams in self-organizing nanowire connectomes.

Self-organizing memristive nanowire connectomes have been exploited for physical (in materia) implementation of brain-inspired computing paradigms. Despite having been shown that the emergent behavior relies on weight plasticity at single junction/synapse level and on 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., physicochemical 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 materia computing, while offering to neuroscientists an alternative platform to understand the role of memory in learning and knowledge.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

Resistive switching and role of interfaces in memristive devices based on amorphous NbOx grown by anodic oxidation.

Memristive devices based on the resistive switching mechanism are continuously attracting attention in the framework of neuromorphic computing and next-generation memory devices. Here, we report on a comprehensive analysis of the resistive switching properties of amorphous NbOx grown by anodic oxidation. Besides a detailed chemical, structural and morphological analysis of the involved materials and interfaces, the mechanism of switching in Nb/NbOx/Au resistive switching cells is discussed by investigating the role of metal-metal oxide interfaces in regulating electronic and ionic transport mechanisms. The resistive switching was found to be related to the formation/rupture of conductive nanofilaments in the NbOx layer under the action of an applied electric field, facilitated by the presence of an oxygen scavenger layer at the Nb/NbOx interface. Electrical characterization including device-to-device variability revealed an endurance >103 full-sweep cycles, retention >104 s, and multilevel capabilities. Furthermore, the observation of quantized conductance supports the physical mechanism of switching based on the formation of atomic-scale conductive filaments. Besides providing new insights into the switching properties of NbOx, this work also highlights the perspective of anodic oxidation as a promising method for the realization of resistive switching cells.

Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices.

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.

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications.

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next-generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic-sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.

Connectome of memristive nanowire networks through graph theory.

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.

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks.

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.

Recommended implementation of electrical resistance tomography for conductivity mapping of metallic nanowire networks using voltage excitation.

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.

Recent Advances in Sequential Infiltration Synthesis (SIS) of Block Copolymers (BCPs).

In the continuous downscaling of device features, the microelectronics industry is facing the intrinsic limits of conventional lithographic techniques. The development of new synthetic approaches for large-scale nanopatterned materials with enhanced performances is therefore required in the pursuit of the fabrication of next-generation devices. Self-assembled materials as block copolymers (BCPs) provide great control on the definition of nanopatterns, promising to be ideal candidates as templates for the selective incorporation of a variety of inorganic materials when combined with sequential infiltration synthesis (SIS). In this review, we report the latest advances in nanostructured inorganic materials synthesized by infiltration of self-assembled BCPs. We report a comprehensive description of the chemical and physical characterization techniques used for in situ studies of the process mechanism and ex situ measurements of the resulting properties of infiltrated polymers. Finally, emerging optical and electrical properties of such materials are discussed.

Water-Mediated Ionic Migration in Memristive Nanowires with a Tunable Resistive Switching Mechanism.

Memristive devices based on electrochemical resistive switching effects have been proposed as promising candidates for in-memory computing and for the realization of artificial neural networks. Despite great efforts toward understanding the nanoionic processes underlying resistive switching phenomena, comprehension of the effect of competing redox processes on device functionalities from the materials perspective still represents a challenge. In this work, we experimentally and theoretically investigate the concurring reactions of silver and moisture and their impact on the electronic properties of a single-crystalline ZnO nanowire (NW). A decrease in electronic conductivity due to surface adsorption of moisture is observed, whereas, at the same time, water molecules reduce the energy barrier for Ag+ ion migration on the NW surface, facilitating the conductive filament formation. By controlling the relative humidity, the ratio of intrinsic electronic conductivity and surface ionic conductivity can be tuned to modulate the device performance. The results achieved on a single-crystalline memristive model system shed new light on the dual nature of the mechanism of how moisture affects resistive switching behavior in memristive devices.

TEM Nanostructural Investigation of Ag-Conductive Filaments in Polycrystalline ZnO-Based Resistive Switching Devices.

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.

Junction properties of single ZnO nanowires with asymmetrical Pt and Cu contacts.

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.

Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities.

The ability for artificially reproducing human brain type signals' processing is one of the main challenges in modern information technology, being one of the milestones for developing global communicating networks and artificial intelligence. Electronic devices termed memristors have been proposed as effective artificial synapses able to emulate the plasticity of biological counterparts. Here we report for the first time a single crystalline nanowire based model system capable of combining all memristive functions - non-volatile bipolar memory, multilevel switching, selector and synaptic operations imitating Ca2+ dynamics of biological synapses. Besides underlying common electrochemical fundamentals of biological and artificial redox-based synapses, a detailed analysis of the memristive mechanism revealed the importance of surfaces and interfaces in crystalline materials. Our work demonstrates the realization of self-assembled, self-limited devices feasible for implementation via bottom up approach, as an attractive solution for the ultimate system miniaturization needed for the hardware realization of brain-inspired systems.

A multi-level memristor based on atomic layer deposition of iron oxide.

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.