Emergent dynamics of neuromorphic nanowire networks

Heat-induced morphological changes in silver nanowires deposited on a patterned silicon substrate

Metallic nanowires (NWs) are sensitive to heat treatment and can split into shorter fragments within minutes at temperatures far below the melting point. This process can hinder the functioning of NW-based devices that are subject to relatively mild temperatures. Commonly, heat-induced fragmentation of NWs is attributed to the interplay between heat-enhanced diffusion and Rayleigh instability. In this work, we demonstrated that contact with the substrate plays an important role in the fragmentation process and can strongly affect the outcome of the heat treatment. We deposited silver NWs onto specially patterned silicon wafers so that some NWs were partially suspended over the holes in the substrate. Then, we performed a series of heat-treatment experiments and found that adhered and suspended parts of NWs behave differently under the heat treatment. Moreover, depending on the heat-treatment process, fragmentation in either adhered or suspended parts can dominate. Experiments were supported by finite element method and molecular dynamics simulations.

Solid polymer electrolyte-based atomic switches: from materials to mechanisms and applications

As miniaturization of semiconductor memory devices is reaching its physical and technological limits, there is a demand for memory technologies that operate on new principles. Atomic switches are nanoionic devices that show repeatable resistive switching between high-resistance and low-resistance states under bias voltage applications, based on the transport of metal ions and redox reactions in solids. Their essential structure consists of an ion conductor sandwiched between electrochemically active and inert electrodes. This review focuses on the resistive switching mechanism of atomic switches that utilize a solid polymer electrolyte (SPE) as the ion conductor. Owing to the superior properties of polymer materials such as mechanical flexibility, compatibility with various substrates, and low fabrication costs, SPE-based atomic switches are a promising candidate for the next-generation of volatile and nonvolatile memories. Herein, we describe their operating mechanisms and key factors for controlling the device performance with different polymer matrices. In particular, the effects of moisture absorption in the polymer matrix on the resistive switching behavior are addressed in detail. As potential applications, atomic switches with inkjet-printed SPE and quantum conductance behavior are described. SPE-based atomic switches also have great potential in use for neuromorphic devices. The development of these devices will be enhanced using nanoarchitectonics concepts, which integrate functional materials and devices.

Silver Nanowire Networks with Moisture-Enhanced Learning Ability

The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.

Self-Assembled Monolayer and Nanoparticles Coenhanced Fragmented Silver Nanowire Network Memristor

Silver nanowire (AgNW) networks with self-assembled structures and synaptic connectivity have been recently reported for constructing neuromorphic memristors. However, resistive switching at the cross-point junctions of the network is unstable due to locally enhanced Joule heating and the Gibbs-Thomson effect, which poses an obstacle to the integration of threshold switching and memory function in the same AgNW memristor. Here, fragmented AgNW networks combined with Ag nanoparticles (AgNPs) and mercapto self-assembled monolayers (SAMs) are devised to construct memristors with stable threshold switching and memory behavior. In the above design, the planar gaps between NW segments are for resistive switching, the AgNPs act as metal islands in the gaps to reduce threshold voltage (Vth) and holding voltage (Vhold), and the SAMs suppress surface atom diffusion to avoid Oswald ripening of the AgNPs, which improves switching stability. The fragmented NW-NP/SAM memristors not only circumvent the side effects of conventional NW-stacked junctions to provide durable threshold switching at >Vth but also exhibit synaptic characteristics such as long-term potentiation at ultralow voltage (≪Vth). The combination of NW segments, nanoparticles, and SAMs blazes a new trail for integrating artificial neurons and synapses in AgNW network memristors.

Engineering the structural and electrical interplay of nanostructured Au resistive switching networks by controlling the forming process

Networks of random-assembled gold clusters produced in the gas phase show resistive switching (RS) activity at room temperature and they are suitable for the fabrication of devices for neuromorphic data processing and classification. Fully connected cluster-assembled nanostructured Au films are characterized by a granular structure rich of interfaces, grain boundaries and crystalline defects. Here we report a systematic characterization of the electroforming process of the cluster-assembled films demonstrating how this process affects the interplay between the nano- and mesoscale film structure and the neuromorphic characteristics of the resistive switching activity. The understanding and the control of the influence of the resistive switching forming process on the organization of specific structures at different scales of the cluster-assembled films, provide the possibility to engineer random-assembled neuromorphic architectures for data processing task.

Online dynamical learning and sequence memory with neuromorphic nanowire networks

Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.

A scalable solution recipe for a Ag-based neuromorphic device

Integration and scalability have posed significant problems in the advancement of brain-inspired intelligent systems. Here, we report a self-formed Ag device fabricated through a chemical dewetting process using an Ag organic precursor, which offers easy processing, scalability, and flexibility to address the above issues to a certain extent. The conditions of spin coating, precursor dilution, and use of solvents were varied to obtain different dewetted structures (broadly classified as bimodal and nearly unimodal). A microscopic study is performed to obtain insight into the dewetting mechanism. The electrical behavior of selected bimodal and nearly unimodal devices is related to the statistical analysis of their microscopic structures. A capacitance model is proposed to relate the threshold voltage (Vth) obtained electrically to the various microscopic parameters. Synaptic functionalities such as short-term potentiation (STP) and long-term potentiation (LTP) were emulated in a representative nearly unimodal and bimodal device, with the bimodal device showing a better performance. One of the cognitive behaviors, associative learning, was emulated in a bimodal device. Scalability is demonstrated by fabricating more than 1000 devices, with 96% exhibiting switching behavior. A flexible device is also fabricated, demonstrating synaptic functionalities (STP and LTP).

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.

Solving classification tasks by a receptron based on nonlinear optical speckle fields

Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of non-linear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, and non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are non-linearly dependent. We show that this model, called "receptron", provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware.

Intermolecular and Electrode-Molecule Bonding in a Single Dimer Junction of Naphthalenethiol as Revealed by Surface-Enhanced Raman Scattering Combined with Transport Measurements

Electron transport through noncovalent interaction is of fundamental and practical importance in nanomaterials and nanodevices. Recent single-molecule studies employing single-molecule junctions have revealed unique electron transport properties through noncovalent interactions, especially those through a π-π interaction. However, the relationship between the junction structure and electron transport remains elusive due to the insufficient knowledge of geometric structures. In this article, we employ surface-enhanced Raman scattering (SERS) synchronized with current-voltage (I-V) measurements to characterize the junction structure, together with the transport properties, of a single dimer and monomer junction of naphthalenethiol, the former of which was formed by the intermolecular π-π interaction. The correlation analysis of the vibrational energy and electrical conductance enables identifying the intermolecular and molecule-electrode interactions in these molecular junctions and, consequently, addressing the transport properties exclusively associated with the π-π interaction. In addition, the analysis achieved discrimination of the interaction between the NT molecule and the Au electrode of the junction, i.e., Au-π interactions through-π coupling and though-space coupling. The power density spectra support the noncovalent character at the interfaces in the molecular junctions. These results demonstrate that the simultaneous SERS and I-V technique provides a unique means for the structural and electrical investigation of noncovalent interactions.

Self-Organized Memristive Ensembles of Nanoparticles Below the Percolation Threshold: Switching Dynamics and Phase Field Description

Percolative memristive networks based on self-organized ensembles of silver and gold nanoparticles are synthesized and investigated. Using cyclic voltammetry, pulse and step voltage excitations, we study switching between memristive and capacitive states below the percolation threshold. The resulting systems demonstrate scale-free (self-similar) temporal dynamics, long-term correlations, and synaptic plasticity. The observed plasticity can be manipulated in a controlled manner. The simplified stochastic model of resistance dynamics in memristive networks is testified. A phase field model based on the Cahn-Hilliard and Ginzburg-Landau equations is proposed to describe the dynamics of a self-organized network during the dissolution of filaments.

Neuromorphic learning, working memory, and metaplasticity in nanowire networks

Nanowire networks (NWNs) mimic the brain's neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed "seven plus or minus two" rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to "synaptic metaplasticity" in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

Memristive and Tunneling Effects in 3D Interconnected Silver Nanowires

A network of silver nanowires (Ag-NWs) is grown by electrodeposition in a nanoporous membrane with interconnected nanopores. This bottom-up approach fabrication method gives a conducting network with a 3D architecture and a high density of Ag-NWs. The network is then functionalized during the etching process, which leads to a high initial resistance as well as memristive behavior. The latter is expected to arise from the creation and the destruction of conducting silver filaments in the functionalized Ag-NW network. Moreover, after several cycles of measurement, the resistance of the network switches from a high-resistance regime in the GΩ range with tunnel conduction to a low-resistance regime presenting negative differential resistance in the kΩ range.

3D Interconnected Magnetic Nanowire Networks as Potential Integrated Multistate Memristors

Interconnected magnetic nanowire (NW) networks offer a promising platform for three-dimensional (3D) information storage and integrated neuromorphic computing. Here we report discrete propagation of magnetic states in interconnected Co nanowire networks driven by magnetic field and current, manifested in distinct magnetoresistance (MR) features. In these networks, when only a few interconnected NWs were measured, multiple MR kinks and local minima were observed, including a significant minimum at a positive field during the descending field sweep. Micromagnetic simulations showed that this unusual feature was due to domain wall (DW) pinning at the NW intersections, which was confirmed by off-axis electron holography imaging. In a complex network with many intersections, sequential switching of nanowire sections separated by interconnects was observed, along with stochastic characteristics. The pinning/depinning of the DWs can be further controlled by the driving current density. These results illustrate the promise of such interconnected networks as integrated multistate memristors.

Uniform Silver Nanowire Patterned Electrode on Robust PEN Substrate Using Poly(2-hydroxyethyl methacrylate) Underlayer

Silver nanowire (AgNW) electrodes are among the most essential flexible transparent electrodes (FTEs) emerging as promising alternatives to brittle indium tin oxide (ITO) electrodes. The polymer comprising the plastic substrate to which the AgNWs are applied must also satisfy the mechanical requirements of the final device and withstand the device processing conditions. However, AgNW-based FTEs have some limitations, such as poor adhesion to coated plastic substrates, surface roughness, and difficulty in patterning. This study demonstrates a new strategy for creating AgNW-based patterned flexible poly(ethylene 2,6-naphthalate) (PEN)-based electrodes with appreciable optical and electrical properties. Introducing poly(2-hydroxyethyl methacrylate) on the PEN substrate enhanced the adhesion between the substrate and AgNWs and improved the dispersibility of the AgNWs. Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) and a small amount of 2,4-hexadiyne-1,6-diol as a photosensitizer were coated onto the AgNW layer to improve the surface roughness and achieve an effective electrode pattern. By varying the AgNW concentration, we could tune the density and thickness of the AgNWs to optimize the sheet resistance and transmittance. Optimized AgNWs with a sheet resistance of 22.6 Ω/□ and transmittance of 92.3% at 550 nm were achieved. A polymer solar cell (PSC) was fabricated to evaluate the characteristics of the device employing the flexible electrodes. This PSC showed not only a high power conversion efficiency of 11.20%, similar to that of ITO-based devices, but also excellent mechanical stability, which is difficult to achieve in ITO-based flexible devices.

Brain-like critical dynamics and long-range temporal correlations in percolating networks of silver nanoparticles and functionality preservation after integration of insulating matrix

Random networks of nanoparticle-based memristive switches enable pathways for emulating highly complex and self-organized synaptic connectivity together with their emergent functional behavior known from biological neuronal networks. They therefore embody a distinct class of neuromorphic hardware architectures and provide an alternative to highly regular arrays of memristors. Especially, networks of memristive nanoparticles (NPs) poised at the percolation threshold are promising due to their capabilities of showing brain-like activity such as critical dynamics or long-range temporal correlation (LRTC), which are closely connected to the computational capabilities in biological neuronal networks. Here, we adapt this concept to networks of Ag-NPs poised at the electrical percolation threshold, where the memristive properties are governed by electro-chemical metallization. We show that critical dynamics and LRTC are preserved although the nature of individual memristive gaps throughout the network is fundamentally changed by filling the gaps with an insulating matrix. The results in this work generate important contributions towards the practical applicability of critical dynamics and LRTC in percolating NP networks by elucidating the consequences of NP network encapsulation, which is considered as an important step towards device integration.

Dynamical stochastic simulation of complex electrical behavior in neuromorphic networks of metallic nanojunctions

Nanostructured Au films fabricated by the assembling of nanoparticles produced in the gas phase have shown properties suitable for neuromorphic computing applications: they are characterized by a non-linear and non-local electrical behavior, featuring switches of the electric resistance whose activation is typically triggered by an applied voltage over a certain threshold. These systems can be considered as complex networks of metallic nanojunctions where thermal effects at the nanoscale cause the continuous rearrangement of regions with low and high electrical resistance. In order to gain a deeper understanding of the electrical properties of this nano granular system, we developed a model based on a large three dimensional regular resistor network with non-linear conduction mechanisms and stochastic updates of conductances. Remarkably, by increasing enough the number of nodes in the network, the features experimentally observed in the electrical conduction properties of nanostructured gold films are qualitatively reproduced in the dynamical behavior of the system. In the activated non-linear conduction regime, our model reproduces also the growing trend, as a function of the subsystem size, of quantities like Mutual and Integrated Information, which have been extracted from the experimental resistance series data via an information theoretic analysis. This indicates that nanostructured Au films (and our model) possess a certain degree of activated interconnection among different areas which, in principle, could be exploited for neuromorphic computing applications.

Reservoir computing with 3D nanowire networks

Networks of nanowires are currently being explored for a range of applications in brain-like (or neuromorphic) computing, and especially in reservoir computing (RC). Fabrication of real-world computing devices requires that the nanowires are deposited sequentially, leading to stacking of the wires on top of each other. However, most simulations of computational tasks using these systems treat the nanowires as 1D objects lying in a perfectly 2D plane - the effect of stacking on RC performance has not yet been established. Here we use detailed simulations to compare the performance of perfectly 2D and quasi-3D (stacked) networks of nanowires in two tasks: memory capacity and nonlinear transformation. We also show that our model of the junctions between nanowires is general enough to describe a wide range of memristive networks, and consider the impact of physically realistic electrode configurations on performance. We show that the various networks and configurations have a strikingly similar performance in RC tasks, which is surprising given their radically different topologies. Our results show that networks with an experimentally achievable number of electrodes perform close to the upper bounds achievable when using the information from every wire. However, we also show important differences, in particular that the quasi-3D networks are more resilient to changes in the input parameters, generalizing better to noisy training data. Since previous literature suggests that topology plays an important role in computing performance, these results may have important implications for future applications of nanowire networks in neuromorphic computing.

In-materio reservoir working at low frequencies in a Ag2S-island network

A Ag₂S-island network is fabricated with surrounding electrodes to enable it to be used as a reservoir for unconventional computing. Local conductance change occurs due to the growth/shrinkage of Ag filaments from/into each Ag₂S island in the reservoir. The growth/shrinkage of Ag filaments is caused by the drift of Ag⁺ cations in each Ag₂S island, which results in a unique non-linear response as a reservoir, especially at lower frequencies. The response of the reservoir is shown to depend on the frequency and amplitude of the input signals. So as to evaluate its capability as a reservoir, logical operations were performed using the subject Ag₂S-island network, with the results showing an accuracy of greater than 99%.

Thermodynamic State Machine Network

We describe a model system—a thermodynamic state machine network—comprising a network of probabilistic, stateful automata that equilibrate according to Boltzmann statistics, exchange codes over unweighted bi-directional edges, update a state transition memory to learn transitions between network ground states, and minimize an action associated with fluctuation trajectories. The model is grounded in four postulates concerning self-organizing, open thermodynamic systems—transport-driven self-organization, scale-integration, input-functionalization, and active equilibration. After sufficient exposure to periodically changing inputs, a diffusive-to-mechanistic phase transition emerges in the network dynamics. The evolved networks show spatial and temporal structures that look much like spiking neural networks, although no such structures were incorporated into the model. Our main contribution is the articulation of the postulates, the development of a thermodynamically motivated methodology addressing them, and the resulting phase transition. As with other machine learning methods, the model is limited by its scalability, generality, and temporality. We use limitations to motivate the development of thermodynamic computers—engineered, thermodynamically self-organizing systems—and comment on efforts to realize them in the context of this work. We offer a different philosophical perspective, thermodynamicalism, addressing the limitations of the model and machine learning in general.

Neuromorphic behaviour in discontinuous metal films

Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple evaporation processes, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex. We further demonstrate that these signals meet established criteria for criticality. We perform a detailed experimental investigation of the atomic-scale switching processes that are responsible for these signals, and show that they mimic the integrate-and-fire mechanism of biological neurons. Using numerical simulations and a simple circuit model, we show that the characteristic features of the switching events are dependent on the network state and the local position of the switch within the complex network. We conclude that discontinuous films provide an interesting potential platform for brain-inspired computing.

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.

Evolution and modulation of Ag filament dynamics within memristive devices based on necklace-like Ag@TiO2nanowire networks

Random nanowire networks (NWNs) are regarded as promising memristive materials for applications in information storage, selectors, and neuromorphic computing. The further insight to understand their resistive switching properties and conduction mechanisms is crucial to realize the full potential of random NWNs. Here, a novel planar memristive device based on necklace-like structure Ag@TiO₂NWN is reported, in which a strategy only using water to tailor the TiO₂shell on Ag core for necklace-like core-shell structure is developed to achieve uniform topology connectivity. With analyzing the influence of compliance current on resistive switching characteristics and further tracing evolution trends of resistance state during the repetitive switching cycles, two distinctive evolution trends of low resistance state failure and high resistance state failure are revealed, which bear resemblance to memory loss and consolidation in biological systems. The underlying conduction mechanisms are related to the modulation of the Ag accumulation dynamics inside the filaments at cross-point junctions within conductive paths of NWNs. An optimizing principle is then proposed to design reproducible and reliable threshold switching devices by tuning the NWN density and electrical stimulation. The optimized threshold switching devices have a high ON/OFF ratio of ∼10⁷with threshold voltage as low as 0.35 V. This work will provide insights into engineering random NWNs for diverse functions by modulating external excitation and optimizing NWN parameters to satisfy specific applications, transforming from neuromorphic systems to threshold switching devices as selectors.

Random nanowire networks: Identification of a current-carrying subset of wires using a modified wall follower algorithm

We mimic random nanowire networks by the homogeneous, isotropic, and random deposition of conductive zero-width sticks onto an insulating substrate. The number density (the number of objects per unit area of the surface) of these sticks is supposed to exceed the percolation threshold, i.e., the system under consideration is a conductor. To identify any current-carrying part (the backbone) of the percolation cluster, we have proposed and implemented a modification of the well-known wall follower algorithm-one type of maze solving algorithm. The advantage of the modified algorithm is its identification of the whole backbone without visiting all the edges. The complexity of the algorithm depends significantly on the structure of the graph and varies from O(sqrt[N_{V}]) to Θ(N_{V}). The algorithm has been applied to backbone identification in networks with different number densities of conducting sticks. We have found that (i) for number densities of sticks above the percolation threshold, the strength of the percolation cluster quickly approaches unity as the number density of the sticks increases; (ii) simultaneously, the percolation cluster becomes identical to its backbone plus simplest dead ends, i.e., edges that are incident to vertices of degree 1. This behavior is consistent with the presented analytical evaluations.

Avalanches and edge-of-chaos learning in neuromorphic nanowire networks

The brain's efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a unique brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network's global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality, as observed in cortical neuronal cultures. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, thus demonstrating the potential for a neuromorphic advantage in information processing.

Information dynamics in neuromorphic nanowire networks

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

Nanowire networks: how does small-world character evolve with dimensionality?

Networks of nanowires are currently under consideration for a wide range of electronic and optoelectronic applications. Nanowire devices are usually made by sequential deposition, which inevitably leads to stacking of the wires on top of one another. Here we demonstrate the effect of stacking on the topology of the resulting networks. We compare perfectly 2D networks with quasi-3D networks, and compare both nanowire networks to the corresponding Watts Strogatz networks, which are standard benchmark systems. By investigating quantities such as clustering, path length, modularity, and small world propensity we show that the connectivity of the quasi-3D networks is significantly different to that of the 2D networks, a result which may have important implications for applications of nanowire networks.

Nanoarchitectonics: what's coming next after nanotechnology?

In science and technology today, the crucial importance of the regulation of nanoscale objects and structures is well recognized. The production of functional material systems using nanoscale units can be achieved via the fusion of nanotechnology with the other research disciplines. This task is a part of the emerging concept of nanoarchitectonics, which is a concept moving beyond the area of nanotechnology. The concept of nanoarchitectonics is supposed to involve the architecting of functional materials using nanoscale units based on the principles of nanotechnology. In this focus article, the essences of nanotechnology and nanoarchitectonics are first explained, together with their historical backgrounds. Then, several examples of material production based on the concept of nanoarchitectonics are introduced via several approaches: (i) from atomic switches to neuromorphic networks; (ii) from atomic nanostructure control to environmental and energy applications; (iii) from interfacial processes to devices; and (iv) from biomolecular assemblies to life science. Finally, perspectives relating to the final goals of the nanoarchitectonics approach are discussed.

Planar and Transparent Memristive Devices Based on Titanium Oxide Coated Silver Nanowire Networks with Tunable Switching Voltage

Threshold switching devices are fundamental active elements in more than Moore approaches, integrating the new generation of non-volatile memory devices. Here, the authors report an in-plane threshold resistive switching device with an on/off ratio above 10⁶ , a low resistance state of 10 to 100 kΩ and a high resistance state of 10 to 100 GΩ. Our devices are based on nanocomposites of silver nanowire networks and titanium oxide, where volatile unipolar threshold switching takes place across the gap left by partially spheroidized nanowires. Device reversibility depends on the titanium oxide thickness, while nanowire network density determines the threshold voltage, which can reach as low as 0.16 V. The switching mechanism is explained through percolation between metal-semiconductor islands, in a combined tunneling conduction mechanism, followed by a Schottky emission generated via Joule heating. The devices are prepared by low-cost, atmospheric pressure, and scalable techniques, enabling their application in printable, flexible, and transparent electronics.

Non-Planar Geometrical Effects on the Magnetoelectrical Signal in a Three-Dimensional Nanomagnetic Circuit

Expanding nanomagnetism and spintronics into three dimensions (3D) offers great opportunities for both fundamental and technological studies. However, probing the influence of complex 3D geometries on magnetoelectrical phenomena poses important experimental and theoretical challenges. In this work, we investigate the magnetoelectrical signals of a ferromagnetic 3D nanodevice integrated into a microelectronic circuit using direct-write nanofabrication. Due to the 3D vectorial nature of both electrical current and magnetization, a complex superposition of several magnetoelectrical effects takes place. By performing electrical measurements under the application of 3D magnetic fields, in combination with macrospin simulations and finite element modeling, we disentangle the superimposed effects, finding how a 3D geometry leads to unusual angular dependences of well-known magnetotransport effects such as the anomalous Hall effect. Crucially, our analysis also reveals a strong role of the noncollinear demagnetizing fields intrinsic to 3D nanostructures, which results in an angular dependent magnon magnetoresistance contributing strongly to the total magnetoelectrical signal. These findings are key to the understanding of 3D spintronic systems and underpin further fundamental and device-based studies.

3D Nanomagnetism in Low Density Interconnected Nanowire Networks

Free-standing, interconnected metallic nanowire networks with densities as low as 40 mg/cm³ have been achieved over centimeter-scale areas, using electrodeposition into polycarbonate membranes that have been ion-tracked at multiple angles. Networks of interconnected magnetic nanowires further provide an exciting platform to explore 3-dimensional nanomagnetism, where their structure, topology, and frustration may be used as additional degrees of freedom to tailor the materials properties. New magnetization reversal mechanisms in cobalt networks are captured by the first-order reversal curve method, which demonstrate the evolution from strong demagnetizing dipolar interactions to intersection-mediated domain wall pinning and propagation, and eventually to shape-anisotropy dominated magnetization reversal. These findings open up new possibilities for 3-dimensional integrated magnetic devices for memory, complex computation, and neuromorphics.

Anomalous electrical conduction and negative temperature coefficient of resistance in nanostructured gold resistive switching films

We report the observation of non-metallic electrical conduction, resistive switching, and a negative temperature coefficient of resistance in nanostructured gold films above the electrical percolation and in strong-coupling regime, from room down to cryogenic temperatures (24 K). Nanostructured continuous gold films are assembled by supersonic cluster beam deposition of Au aggregates formed in the gas phase. The structure of the cluster-assembled films is characterized by an extremely high density of randomly oriented crystalline nanodomains, separated by grain boundaries and with a large number of lattice defects. Our data indicates that space charge limited conduction and Coulomb blockade are at the origin of the anomalous electrical behavior. The high density of extended defects and grain boundaries causes the localization of conduction electrons over the entire investigated temperature range.

Sleep-Dependent Memory Consolidation in a Neuromorphic Nanowire Network

A neuromorphic network composed of silver nanowires coated with TiO₂ is found to show certain parallels with neural networks in nature such as biological brains. Owing to the memristive properties emerging at nanowire-to-nanowire contacts, where the Ag/TiO₂/Ag interface exists, the network can store information in the form of connectivity between nanowires in the network as electrically measured as an increase in conductance. The observed memory arises from an interplay between the topological constraints imposed by a complex network structure and the plasticity of its constituting memristive Ag/TiO₂/Ag junctions. Regarding the long-term decay of the connectivity in the network, we further investigate the controllability of the established connectivity. Inspired by the regulated activity cycles of the human brain during sleep, a learning-sleep-recovery cycle was mimicked by applying voltage pulses, with controlling pulse heights and duty ratios, to the nanowire network. Interestingly, even when the conductance was lost during sleep, the network could quickly recover previous states of conductance in the recovery process after sleep. Comparison between results of experiments and theoretical simulations revealed that such a quick recovery of conductance can be realized by sparse voltage pulse application during sleep; in other words, sleep-dependent memory consolidation occurs and can be controlled. The present results provide clues to new learning designs in neuromorphic networks for achieving longer memory retention for future neuromorphic technology.

Neuromorphic Computing Using Emerging Synaptic Devices: A Retrospective Summary and an Outlook

In this paper, emerging memory devices are investigated for a promising synaptic device of neuromorphic computing. Because the neuromorphic computing hardware requires high memory density, fast speed, and low power as well as a unique characteristic that simulates the function of learning by imitating the process of the human brain, memristor devices are considered as a promising candidate because of their desirable characteristic. Among them, Phase-change RAM (PRAM) Resistive RAM (ReRAM), Magnetic RAM (MRAM), and Atomic Switch Network (ASN) are selected to review. Even if the memristor devices show such characteristics, the inherent error by their physical properties needs to be resolved. This paper suggests adopting an approximate computing approach to deal with the error without degrading the advantages of emerging memory devices.

Topological Properties of Neuromorphic Nanowire Networks

Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and functional properties of human neural networks. Here, we apply graph theory to a model of a novel neuromorphic system constructed from self-assembled nanowires, whose structure and function may mimic that of human neural networks. Simulations of neuromorphic nanowire networks allow us to directly examine their topology at the individual nanowire–node scale. This type of investigation is currently extremely difficult experimentally. We then apply network cartographic approaches to compare neuromorphic nanowire networks with: random networks (including an untrained artificial neural network); grid-like networks and the structural network of C. elegans. Our results demonstrate that neuromorphic nanowire networks exhibit a small–world architecture similar to the biological system of C. elegans, and significantly different from random and grid-like networks. Furthermore, neuromorphic nanowire networks appear more segregated and modular than random, grid-like and simple biological networks and more clustered than artificial neural networks. Given the inextricable link between structure and function in neural networks, these results may have important implications for mimicking cognitive functions in neuromorphic nanowire networks.