Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing

True random number generation using the spin crossover in LaCoO3

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO3 that is electrically biased within its spin crossover regime. The LaCoO3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

An artificial visual neuron with multiplexed rate and time-to-first-spike coding

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.

Fully Memristive Elementary Motion Detectors for a Maneuver Prediction

Insects can efficiently perform object motion detection via a specialized neural circuit, called an elementary motion detector (EMD). In contrast, conventional machine vision systems require significant computational resources for dynamic motion processing. Here, a fully memristive EMD (M-EMD) is presented that implements the Hassenstein-Reichardt (HR) correlator, a biological model of the EMD. The M-EMD consists of a simple Wye (Y) configuration, including a static resistor, a dynamic memristor, and a Mott memristor. The resistor and dynamic memristor introduce different signal delays, enabling spatio-temporal signal integration in the subsequent Mott memristor, resulting in a direction-selective response. In addition, a neuromorphic system is developed employing the M-EMDs to predict a lane-changing maneuver by vehicles on the road. The system achieved a high accuracy (> 87%) in predicting future lane-changing maneuvers on the Next Generation Simulation (NGSIM) dataset while reducing the computational cost by 92.9% compared to the conventional neuromorphic system without the M-EMD, suggesting its strong potential for edge-level computing.

Memristive Explainable Artificial Intelligence Hardware

Artificial intelligence (AI) is often considered a black box because it provides optimal answers without clear insight into its decision-making process. To address this black box problem, explainable artificial intelligence (XAI) has emerged, which provides an explanation and interpretation of its decisions, thereby promoting the trustworthiness of AI systems. Here, a memristive XAI hardware framework is presented. This framework incorporates three distinct types of memristors (Mott memristor, valence change memristor, and charge trap memristor), each responsible for performing three essential functions (perturbation, analog multiplication, and integration) required for the XAI hardware implementation. Three memristor arrays with high robustness are fabricated and the image recognition of 3 × 3 testing patterns and their explanation map generation are experimentally demonstrated. Then, a software-based extended system based on the characteristics of this hardware is built, simulating a large-scale image recognition task. The proposed system can perform the XAI operations with only 4.32% of the energy compared to conventional digital systems, enlightening its strong potential for the XAI accelerator.

High-order sensory processing nanocircuit based on coupled VO2 oscillators

Conventional circuit elements are constrained by limitations in area and power efficiency at processing physical signals. Recently, researchers have delved into high-order dynamics and coupled oscillation dynamics utilizing Mott devices, revealing potent nonlinear computing capabilities. However, the intricate yet manageable population dynamics of multiple artificial sensory neurons with spatiotemporal coupling remain unexplored. Here, we present an experimental hardware demonstration featuring a capacitance-coupled VO2 phase-change oscillatory network. This network serves as a continuous-time dynamic system for sensory pre-processing and encodes information in phase differences. Besides, a decision-making module for special post-processing through software simulation is designed to complete a bio-inspired dynamic sensory system. Our experiments provide compelling evidence that this transistor-free coupling network excels in sensory processing tasks such as touch recognition and gesture recognition, achieving significant advantages of fewer devices and lower energy-delay-product compared to conventional methods. This work paves the way towards an efficient and compact neuromorphic sensory system based on nano-scale nonlinear dynamics.

Interfacial Resistive Switching of Niobium-Titanium Anodic Memristors with Self-Rectifying Capabilities

A broad compositional range of Nb-Ti anodic memristors with volatile and self-rectifying behaviour was studied using a combinatorial screening approach. A Nb-Ti thin-film combinatorial library was co-deposited by sputtering, serving as the bottom electrode for the memristive devices. The library, with a compositional spread ranging between 22 and 64 at.% Ti was anodically oxidised, the mixed oxide being the active layer in MIM-type structures completed by Pt discreet top electrode patterning. By studying I-U sweeps, memristors with self-rectifying and volatile behaviour were identified. Moreover, all the analysed memristors demonstrated multilevel properties. The best-performing memristors showed HRS/LRS (high resistive state/low resistive state) ratios between 4 and 6 × 105 and very good retention up to 106 successive readings. The anodic memristors grown along the compositional spread showed very good endurance up to 106 switching cycles, excluding those grown from alloys containing between 31 and 39 at.% Ti, which withstood only 10 switching cycles. Taking into consideration all the parameters studied, the Nb-46 at.% Ti composition was screened as the parent metal alloy composition, leading to the best-performing anodic memristor in this alloy system. The results obtained suggest that memristive behaviour is based on an interfacial non-filamentary type of resistive switching, which is consistent with the performed cross-sectional TEM structural and chemical characterisation.

Light-Triggerable and Gate-Tunable Negative Differential Resistance in Small Molecules Heterojunction

Negative differential resistance (NDR), a phenomenon in which the current decreases when the applied voltage is increased, is attracting attention as a unique electrical property. Here, we propose a broad spectral photo/gate cotunable channel switching NDR (CS-NDR) device. The proposed CS-NDR device has superior linear gate-tunable NDR behavior and highly reproducible properties compared to the previously reported NDR devices, as the fundamental mechanism of the CS-NDR device is directly related to a charge transport channel switching by the linear increase of the applied drain voltage. We also experimentally demonstrate that the photoinduced NDR behavior of the CS-NDR device was derived from the grain boundaries of dinaphtho[2;3-b:2',3'-f]-thieno[3,2-b]thiophene. Furthermore, this work produces a 9 × 9 CS-NDR device array composed of 81 devices, providing the reproducibility and uniformity of the CS-NDR device. Finally, we successfully demonstrate the detection of text images with 81 CS-NDR devices using the proposed photo/gate cotunable NDR behavior.

Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called "thermal neuristors", which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

Chalcogenide Ovonic Threshold Switching Selector

Today's explosion of data urgently requires memory technologies capable of storing large volumes of data in shorter time frames, a feat unattainable with Flash or DRAM. Intel Optane, commonly referred to as three-dimensional phase change memory, stands out as one of the most promising candidates. The Optane with cross-point architecture is constructed through layering a storage element and a selector known as the ovonic threshold switch (OTS). The OTS device, which employs chalcogenide film, has thereby gathered increased attention in recent years. In this paper, we begin by providing a brief introduction to the discovery process of the OTS phenomenon. Subsequently, we summarize the key electrical parameters of OTS devices and delve into recent explorations of OTS materials, which are categorized as Se-based, Te-based, and S-based material systems. Furthermore, we discuss various models for the OTS switching mechanism, including field-induced nucleation model, as well as several carrier injection models. Additionally, we review the progress and innovations in OTS mechanism research. Finally, we highlight the successful application of OTS devices in three-dimensional high-density memory and offer insights into their promising performance and extensive prospects in emerging applications, such as self-selecting memory and neuromorphic computing.

Highly Stable HfO2 Memristors through van der Waals Electrode Lamination and Delamination

Memristors have attracted considerable attention in the past decade, holding great promise for future neuromorphic computing. However, the intrinsic poor stability and large device variability remain key limitations for practical application. Here, we report a simple method to directly visualize the origin of poor stability. By mechanically removing the top electrodes of memristors operated at different states (such as SET or RESET), the memristive layer could be exposed and directly characterized through conductive atomic force microscopy, providing two-dimensional area information within memristors. Based on this technique, we observed the existence of multiple conducting filaments during the formation process and built up a physical model between filament numbers and the cycle-to-cycle variation. Furthermore, by improving the interface quality through the van der Waals top electrode, we could reduce the filament number down to a single filament during all switching cycles, leading to much controlled switching behavior and reliable device operation.

Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit

Energy-based computing is a promising approach for addressing the rising demand for solving NP-hard problems across diverse domains, including logistics, artificial intelligence, cryptography, and optimization. Probabilistic computing utilizing pbits, which can be manufactured using the semiconductor process and seamlessly integrated with conventional processing units, stands out as an efficient candidate to meet these demands. Here, we propose a novel pbit unit using an NbOx volatile memristor-based oscillator capable of generating probabilistic bits in a self-clocking manner. The noise-induced metal-insulator transition causes the probabilistic behavior, which can be effectively modeled using a multi-noise-induced stochastic process around the metal-insulator transition temperature. We demonstrate a memristive Boltzmann machine based on our proposed pbit and validate its feasibility by solving NP-hard problems. Furthermore, we propose a streamlined operation methodology that considers the autocorrelation of individual bits, enabling energy-efficient and high-performance probabilistic computing.

Generative complex networks within a dynamic memristor with intrinsic variability

Artificial neural networks (ANNs) have gained considerable momentum in the past decade. Although at first the main task of the ANN paradigm was to tune the connection weights in fixed-architecture networks, there has recently been growing interest in evolving network architectures toward the goal of creating artificial general intelligence. Lagging behind this trend, current ANN hardware struggles for a balance between flexibility and efficiency but cannot achieve both. Here, we report on a novel approach for the on-demand generation of complex networks within a single memristor where multiple virtual nodes are created by time multiplexing and the non-trivial topological features, such as small-worldness, are generated by exploiting device dynamics with intrinsic cycle-to-cycle variability. When used for reservoir computing, memristive complex networks can achieve a noticeable increase in memory capacity a and respectable performance boost compared to conventional reservoirs trivially implemented as fully connected networks. This work expands the functionality of memristors for ANN computing.

Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar

Combinatorial optimization problems are prevalent in various fields, but obtaining exact solutions remains challenging due to the combinatorial explosion with increasing problem size. Special-purpose hardware such as Ising machines, particularly memristor-based analog Ising machines, have emerged as promising solutions. However, existing simulate-annealing-based implementations have not fully exploited the inherent parallelism and analog storage/processing features of memristor crossbar arrays. This work proposes a quantum-inspired parallel annealing method that enables full parallelism and improves solution quality, resulting in significant speed and energy improvement when implemented in analog memristor crossbars. We experimentally solved tasks, including unweighted and weighted Max-Cut and traveling salesman problem, using our integrated memristor chip. The quantum-inspired parallel annealing method implemented in memristor-based hardware has demonstrated significant improvements in time- and energy-efficiency compared to previously reported simulated annealing and Ising machine implemented on other technologies. This is because our approach effectively exploits the natural parallelism, analog conductance states, and all-to-all connection provided by memristor technology, promising its potential for solving complex optimization problems with greater efficiency.

Stochastic transition in synchronized spiking nanooscillators

This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.

Complex Oxides for Brain-Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.

ECRAM Materials, Devices, Circuits and Architectures: A Perspective

Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.

A Memristors-Based Dendritic Neuron for High-Efficiency Spatial-Temporal Information Processing

Diverse microscopic ionic dynamics help mediate the ability of a biological neural network to handle complex tasks with low energy consumption. Thus, rich internal ionic dynamics in memristors based on transition metal oxide are expected to provide a unique and useful platform for implementing energy-efficient neuromorphic computing. To this end, a titanium oxide (TiOx )-based interface-type dynamic memristor and an niobium oxide (NbOx )-based Mott memristor are integrated as an artificial dendrite and spike-firing soma, respectively, to construct a dendritic neuron unit for realizing high-efficiency spatial-temporal information processing. Further, a dendritic neural network is hardware-implemented for spatial-temporal information processing to highlight the computational advantages achieved by incorporating dendritic functions in the network. Human motion recognition is demonstrated using the Nanyang Technological University-Red Green Blue (NTU-RGB) dataset as a benchmark spatial-temporal task; it shows a nearly 20% improvement in accuracy for the memristors-based hardware incorporating dendrites and a 1000× advantage in power efficiency compared to that of the graphics processing unit (GPU). The dendritic neuron developed in this study can be considered a critical building block for implementing more bio-plausible neural networks that can manage complex spatial-temporal tasks with high efficiency.

Harnessing the Metal-Insulator Transition of VO2 in Neuromorphic Computing

Future-generation neuromorphic computing seeks to overcome the limitations of von Neumann architectures by colocating logic and memory functions, thereby emulating the function of neurons and synapses in the human brain. Despite remarkable demonstrations of high-fidelity neuronal emulation, the predictive design of neuromorphic circuits starting from knowledge of material transformations remains challenging. VO2 is an attractive candidate since it manifests a near-room-temperature, discontinuous, and hysteretic metal-insulator transition. The transition provides a nonlinear dynamical response to input signals, as needed to construct neuronal circuit elements. Strategies for tuning the transformation characteristics of VO2 based on modification of material properties, interfacial structure, and field couplings, are discussed. Dynamical modulation of transformation characteristics through in situ processing is discussed as a means of imbuing synaptic function. Mechanistic understanding of site-selective modification; external, epitaxial, and chemical strain; defect dynamics; and interfacial field coupling in modifying local atomistic structure, the implications therein for electronic structure, and ultimately, the tuning of transformation characteristics, is emphasized. Opportunities are highlighted for inverse design and for using design principles related to thermodynamics and kinetics of electronic transitions learned from VO2 to inform the design of new Mott materials, as well as to go beyond energy-efficient computation to manifest intelligence.

Energy and Space Efficient Parallel Adder Using Molecular Memristors

A breakthrough in in-memory computing technologies hinges on the development of appropriate material platforms that can overcome their existing limitations, such as larger than optimal footprint and multiple serial computational steps, with potential accumulation of errors. Using a molecular switching element with multiple non-monotonic and deterministic transitions, the device count and the number of computational steps can be substantially reduced. With molecular materials, however, the realization of a reliable and robust platform is an unattained goal for decades. Here, crossbar arrays with up to 64 molecular memristors are fabricated to experimentally demonstrate 8-bit serial and 4-bit parallel adders that operate for thousands of measurement cycles with an estimated error probability of 10-16 . For performance benchmarking, a 32-bit parallel adder is designed and simulated with 268 million inputs including contributions from the peripheral circuitry showing a 47× higher energy efficiency, 93× faster operation, and 9% of the footprint, leading to 4390 times improved energy-delay product compared to a special purpose complementary metal-oxide-semiconductor (CMOS)-based multicore adder.

High Amplitude Spike Generator in Au Nanodot-Incorporated NbOx Mott Memristor

NbO_x-based Mott memristors exhibit fast threshold switching behaviors, making them suitable for spike generators in neuromorphic computing and stochastic clock generators in security devices. In these applications, a high output spike amplitude is necessary for threshold level control and accurate signal detection. Here, we propose a materialwise solution to obtain the high amplitude spikes by inserting Au nanodots into the NbO_x device. The Au nanodots enable increasing the threshold voltage by modulating the oxygen contents at the electrode-oxide interface, providing a higher ON current compared to nanodot-free NbO_x devices. Also, the reduction of the local switching region volume decreases the thermal capacitance of the system, allowing the maximum spike amplitude generation. Consequently, the Au nanodot incorporation increases the spike amplitude of the NbO_x device by 6 times, without any additional external circuit elements. The results are systematically supported by both a numerical model and a finite-element-method-based multiphysics model.

Interface Effects of Strain-Energy Potentials on Phase Transition Characteristics of VO2 Thin-Films

Metal-insulator-transition (MIT) of VO₂ has attracted strong attention as a potential phenomenon to be utilized in nanostructured devices. Dynamics of MIT phase transition determines the feasibility of VO₂ material properties in various applications, for example, photonic components, sensors, MEMS actuators, and neuromorphic computing. However, conventional interface strain model predicts the MIT effect accurately for bulk, but fairly for the thin films, and thus, a new model is needed. It was found that the VO₂ thin film-substrate interface plays a crucial role in determining transition dynamics properties. In VO₂ thin films on different substrates, coexistence of insulator-state polymorph phases, dislocations, and a few unit cell reconstruction layer form an interface structure minimizing strain energy by the increase of structural complexity. As a consequence, MIT temperature and hysteresis of structure increased as the transition enthalpy of the interface increased. Thus, the process does not obey the conventional Clausius-Clapeyron law anymore. A new model is proposed for residual strain energy potentials by implementing a modified Cauchy strain. Experimental results confirm that the MIT effect in constrained VO₂ thin films is induced through the Peierls mechanism. The developed model provides tools for strain engineering in the atomic scale for crystal potential distortion effects in nanotechnology, such as topological quantum devices.

In-memory factorization of holographic perceptual representations

Disentangling the attributes of a sensory signal is central to sensory perception and cognition and hence is a critical task for future artificial intelligence systems. Here we present a compute engine capable of efficiently factorizing high-dimensional holographic representations of combinations of such attributes, by exploiting the computation-in-superposition capability of brain-inspired hyperdimensional computing, and the intrinsic stochasticity associated with analogue in-memory computing based on nanoscale memristive devices. Such an iterative in-memory factorizer is shown to solve at least five orders of magnitude larger problems that cannot be solved otherwise, as well as substantially lowering the computational time and space complexity. We present a large-scale experimental demonstration of the factorizer by employing two in-memory compute chips based on phase-change memristive devices. The dominant matrix-vector multiplication operations take a constant time, irrespective of the size of the matrix, thus reducing the computational time complexity to merely the number of iterations. Moreover, we experimentally demonstrate the ability to reliably and efficiently factorize visual perceptual representations.

CMOS-compatible electro-optical SRAM cavity device based on negative differential resistance

The impending collapse of Moore-like growth of computational power has spurred the development of alternative computing architectures, such as optical or electro-optical computing. However, many of the current demonstrations in literature are not compatible with the dominant complementary metal-oxide semiconductor (CMOS) technology used in large-scale manufacturing today. Here, inspired by the famous Esaki diode demonstrating negative differential resistance (NDR), we show a fully CMOS-compatible electro-optical memory device, based on a new type of NDR diode. This new diode is based on a horizontal PN junction in silicon with a unique layout providing the NDR feature, and we show how it can easily be implemented into a photonic micro-ring resonator to enable a bistable device with a fully optical readout in the telecom regime. Our result is an important stepping stone on the way to new nonlinear electro-optic and neuromorphic computing structures based on this new NDR diode.

Discontinuous Event-Triggered Control for Local Stabilization of Memristive Neural Networks With Actuator Saturation: Discrete- and Continuous-Time Lyapunov Methods

In this article, the local stabilization problem is investigated for a class of memristive neural networks (MNNs) with communication bandwidth constraints and actuator saturation. To overcome these challenges, a discontinuous event-trigger (DET) scheme, consisting of the rest interval and work interval, is proposed to cut down the triggering times and save the limited communication resources. Then, a novel relaxed piecewise functional is constructed for closed-loop MNNs. The main advantage of the designed functional consists in that it is positive definite only in the work intervals and the sampling instants but not necessarily inside the rest intervals. With the aid of extended reciprocally convex combination lemma, generalized sector condition, and some inequality techniques, two local stabilization criteria are established on the basis of both the discrete- and continuous-time Lyapunov methods. The proposed analysis technique fully takes advantage of the looped-functional and the event-trigger mechanism. Moreover, two optimization schemes are, respectively, established to design the control gain and enlarge the estimates of the admissible initial conditions (AICs) and the upper bound of rest intervals. Finally, some comparison results are given to validate the superiority of the proposed method.

Physical Origin of Negative Differential Resistance in V3 O5 and Its Application as a Solid-State Oscillator

Oxides that exhibit an insulator-metal transition can be used to fabricate energy-efficient relaxation oscillators for use in hardware-based neural networks but there are very few oxides with transition temperatures above room temperature. Here the structural, electrical, and thermal properties of V₃ O₅ thin films and their application as the functional oxide in metal/oxide/metal relaxation oscillators are reported. The V₃ O₅ devices show electroforming-free volatile threshold switching and negative differential resistance (NDR) with stable (<3% variation) cycle-to-cycle operation. The physical mechanisms underpinning these characteristics are investigated using a combination of electrical measurements, in situ thermal imaging, and device modeling. This shows that conduction is confined to a narrow filamentary path due to self-confinement of the current distribution and that the NDR response is initiated at temperatures well below the insulator-metal transition temperature where it is dominated by the temperature-dependent conductivity of the insulating phase. Finally, the dynamics of individual and coupled V₃ O₅ -based relaxation oscillators is reported, showing that capacitively coupled devices exhibit rich non-linear dynamics, including frequency and phase synchronization. These results establish V₃ O₅ as a new functional material for volatile threshold switching and advance the development of robust solid-state neurons for neuromorphic computing.

Biphasic action potential and chaos in a symmetrical Chua Corsage Memristor-based circuit

Neuromorphic computing provides unique computing and memory capabilities that could break the limitation of conventional von Neumann computing. Toward realizing neuromorphic computing, fabrication and synthetization of hardware elements and circuits to emulate biological neurons are crucial. Despite the striking progress in exploring neuron circuits, the existing circuits can only reproduce monophasic action potentials, and no studies report on circuits that could emulate biphasic action potentials, limiting the development of neuromorphic devices. Here, we present a simple third-order memristive circuit built with a classical symmetrical Chua Corsage Memristor (SCCM) to accurately emulate biological neurons and show that the circuit can reproduce monophasic action potentials, biphasic action potentials, and chaos. Applying the edge of chaos criterion, we calculate that the SCCM and the proposed circuit have the symmetrical edge of chaos domains with respect to the origin, which plays an important role in generating biphasic action potentials. Also, we draw a parameter classification map of the proposed circuit, showing the edge of chaos domain (EOCD), the locally active domain, and the locally passive domain. Near the calculated EOCD, the third-order circuit generates monophasic action potentials, biphasic action potentials, chaos, and ten types of symmetrical bi-directional neuromorphic phenomena by only tuning the input voltage, showing a resemblance to biological neurons. Finally, a physical SCCM circuit and some experimentally measured neuromorphic waveforms are exhibited. The experimental results agree with the numerical simulations, verifying that the proposed circuit is suitable as artificial neurons.

Emerging memristive neurons for neuromorphic computing and sensing

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

Edge-of-chaos learning achieved by ion-electron-coupled dynamics in an ion-gating reservoir

Physical reservoir computing has recently been attracting attention for its ability to substantially reduce the computational resources required to process time series data. However, the physical reservoirs that have been reported to date have had insufficient computational capacity, and most of them have a large volume, which makes their practical application difficult. Here, we describe the development of a Li⁺ electrolyte-based ion-gating reservoir (IGR), with ion-electron-coupled dynamics, for use in high-performance physical reservoir computing. A variety of synaptic responses were obtained in response to past experience, which were stored as transient charge density patterns in an electric double layer, at the Li⁺ electrolyte/diamond interface. Performance for a second-order nonlinear dynamical equation task is one order of magnitude higher than memristor-based reservoirs. The edge-of-chaos state of the IGR enabled the best computational capacity. The IGR described here opens the way for high-performance and integrated neural network devices.

Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems

Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.

Emulating the Signal Transmission in a Neural System Using Polymer Membranes

Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.

Ag/HfOx/Pt Unipolar Memristor for High-Efficiency Logic Operation

Unipolar resistive switching (URS) behavior, known as the SET and RESET operating in a single voltage sweep direction, has shown great potential in the simplification of the peripheral circuit. The URS memristor always involves complicated interfacial engineering and structural design. In this work, a reliable URS behavior is realized using a simple Ag/HfO_x/Pt memristor structure. The memristor displays a retention time of >10⁴ s, an ON/OFF ratio of >10³, and a good operation voltage. Synergy and competition between the Ag conductive filament formed by redox reaction and the migration of an oxygen vacancy are responsible for the observed URS. By comparison, a 35% power consumption is reduced during the logical operation from 0 to 1 to 0. The operation strategy is demonstrated by exhibiting the ACSII code of the capital letter denoted by eight logic states. This work provides a low-power concept for ultrahigh data storage using the URS memristor.

Action potential and chaos near the edge of chaos in memristive circuits

Memristor-based neuromorphic systems have a neuro-bionic function, which is critical for possibly overcoming Moore's law limitation and the von Neumann bottleneck problem. To explore neural behaviors and complexity mechanisms in memristive circuits, this paper proposes an N-type locally active memristor, based on which a third-order memristive circuit is constructed. Theoretical analysis shows that the memristive circuit can exhibit not only various action potentials but also self-sustained oscillation and chaos. Based on Chua's theory of local activity, this paper finds that the neural behaviors and chaos emerge near the edge of chaos through subcritical Hopf bifurcation, in which the small unstable limit cycle is depicted by the dividing line between the attraction basin of the large stable limit cycle and the attraction basin of the stable equilibrium point. Furthermore, an analog circuit is designed to imitate the action potentials and chaos, and the simulation results are in agreement with the theoretical analysis.

Antimonotonicity, Hysteresis and Coexisting Attractors in a Shinriki Circuit with a Physical Memristor as a Nonlinear Resistor

A novel approach to the physical memristor’s behavior of the KNOWM is presented in this work. The KNOWM’s memristor’s intrinsic feature encourages its use as a nonlinear resistor in chaotic circuits. Furthermore, this memristor has been shown to act like a static nonlinear resistor under certain situations. Consequently, for the first time, the KNOWM memristor is used as a static nonlinear resistor in the well-known chaotic Shinriki oscillator. In order to examine the circuit’s dynamical behavior, a host of nonlinear simulation tools, such as phase portraits, bifurcation and continuation diagrams, as well as a maximal Lyapunov exponent diagram, are used. Interesting phenomena related to chaos theory are observed. More specifically, the entrance to chaotic behavior through the antimonotonicity phenomenon is observed. Furthermore, the hysteresis phenomenon, as well as the existence of coexisting attractors in regards to the initial conditions and the parameters of the system, are investigated. Moreover, the period-doubling route to chaos and crisis phenomena are observed too.

A Passive but Local Active Memristor and Its Complex Dynamics

This paper proposes a globally passive but locally active memristor, which has three stable equilibrium points and two unstable equilibrium points, exhibiting two stable locally active regions and four unstable locally active regions. We find that when the memristor operates in a stable local active region, the memristor-based second-order circuit with a parallel capacitor or a series inductor can produce periodic oscillation. Moreover, the memristor-based third-order circuit with two energy storage elements, a capacitor and an inductor, can produce complex chaotic oscillation, forming the simplest chaotic circuit.

Memristive technologies for data storage, computation, encryption, and radio-frequency communication

Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non-von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here.

Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse

Neuromorphic computing targets the hardware embodiment of neural network, and device implementation of individual neuron and synapse has attracted considerable attention. The emulation of synaptic plasticity has shown promising results after the advent of memristors. However, neuronal intrinsic plasticity, which involves in learning process through interactions with synaptic plasticity, has been rarely demonstrated. Synaptic and intrinsic plasticity occur concomitantly in learning process, suggesting the need of the simultaneous implementation. Here, we report a neurosynaptic device that mimics synaptic and intrinsic plasticity concomitantly in a single cell. Threshold switch and phase change memory are merged in threshold switch-phase change memory device. Neuronal intrinsic plasticity is demonstrated based on bottom threshold switch layer, which resembles the modulation of firing frequency in biological neuron. Synaptic plasticity is also introduced through the nonvolatile switching of top phase change layer. Intrinsic and synaptic plasticity are simultaneously emulated in a single cell to establish the positive feedback between them. A positive feedback learning loop which mimics the retraining process in biological system is implemented in threshold switch-phase change memory array for accelerated training.

Synaptic plasticity and neuronal intrinsic plasticity are both involved in the learning process of hardware artificial neural network. Here, Lee et al. integrate a threshold switch and a phase change memory in a single device, which emulates biological synaptic and intrinsic plasticity simultaneously.

Thermal Conductivity of Amorphous NbOx Thin Films and Its Effect on Volatile Memristive Switching

Metal-oxide-metal (MOM) devices based on niobium oxide exhibit threshold switching (or current-controlled negative differential resistance) due to thermally induced conductivity changes produced by Joule heating. A detailed understanding of the device characteristics therefore relies on an understanding of the thermal properties of the niobium oxide film and the MOM device structure. In this study, we use time-domain thermoreflectance to determine the thermal conductivity of amorphous NbO_x films as a function of film composition and temperature. The thermal conductivity is shown to vary between 0.86 and 1.25 W·m^-1·K^-1 over the composition (x = 1.9 to 2.5) and temperature (293 to 453 K) ranges examined, and to increase with temperature for all compositions. The impact of these thermal conductivity variations on the quasistatic current-voltage (I-V) characteristics and oscillator dynamics of MOM devices is then investigated using a lumped-element circuit model. Understanding such effects is essential for engineering functional devices for nonvolatile memory and brain-inspired computing applications.

Phase-change memtransistive synapses for mixed-plasticity neural computations

In the mammalian nervous system, various synaptic plasticity rules act, either individually or synergistically, over wide-ranging timescales to enable learning and memory formation. Hence, in neuromorphic computing platforms, there is a significant need for artificial synapses that can faithfully express such multi-timescale plasticity mechanisms. Although some plasticity rules have been emulated with elaborate complementary metal oxide semiconductor and memristive circuitry, device-level hardware realizations of long-term and short-term plasticity with tunable dynamics are lacking. Here we introduce a phase-change memtransistive synapse that leverages both the non-volatility of the phase configurations and the volatility of field-effect modulation for implementing tunable plasticities. We show that these mixed-plasticity synapses can enable plasticity rules such as short-term spike-timing-dependent plasticity that helps with the modelling of dynamic environments. Further, we demonstrate the efficacy of the memtransistive synapses in realizing accelerators for Hopfield neural networks for solving combinatorial optimization problems.

Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization

A self-organizing map (SOM) is a powerful unsupervised learning neural network for analyzing high-dimensional data in various applications. However, hardware implementation of SOM is challenging because of the complexity in calculating the similarities and determining neighborhoods. We experimentally demonstrated a memristor-based SOM based on Ta/TaO_x/Pt 1T1R chips for the first time, which has advantages in computing speed, throughput, and energy efficiency compared with the CMOS digital counterpart, by utilizing the topological structure of the array and physical laws for computing without complicated circuits. We employed additional rows in the crossbar arrays and identified the best matching units by directly calculating the similarities between the input vectors and the weight matrix in the hardware. Using the memristor-based SOM, we demonstrated data clustering, image processing and solved the traveling salesman problem with much-improved energy efficiency and computing throughput. The physical implementation of SOM in memristor crossbar arrays extends the capability of memristor-based neuromorphic computing systems in machine learning and artificial intelligence.

Self-organizing maps are data mining tools for unsupervised learning algorithms dealing with big data problems. The authors experimentally demonstrate a memristor-based self-organizing map that is more efficient in computing speed and energy consumption for data clustering, image processing and solving optimization problems.

Brain-inspired computing needs a master plan

New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realize this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing?

Memristive Behaviors Dominated by Reversible Nucleation Dynamics of Phase-Change Nanoclusters

One of the important steps for realizing artificial intelligence is identifying elementary units that are beneficial for neural network construction. A type of memristive behavior in which phase-change nanoclusters nucleate adaptively in two adjacent dielectric layers with distinct distribution patterns is demonstrated. This memristive system responds in potentiation to increased stimulation strength and fire action potential after threshold stimulation. Reversible nucleation of phase-change nanoclusters is confirmed after both in situ and ex situ examinations using high-resolution transmission electron microscopy. The dynamics at the nanoscale level dominates the actions of the two dielectric layers. The oscillation response over a long period is due to the competition between crystalline and amorphous phases in the layer near the bottom electrode. Weight mutation, that is, action potential firing, is caused by the blockage of the filament in the layer near the top electrode. The memristive system is compact and able to execute complicated functions of a complete neuron and performs an important role in neuromorphic computing.

Complex Oscillations of Chua Corsage Memristor with Two Symmetrical Locally Active Domains

This paper proposes a modified Chua Corsage Memristor endowed with two symmetrical locally active domains. Under the DC bias voltage in the locally active domains, the memristor with an inductor can construct a second-order circuit to generate periodic oscillation. Based on the theories of the edge of chaos and local activity, the oscillation mechanism of the symmetrical periodic oscillations of the circuit is revealed. The third-order memristor circuit is constructed by adding a passive capacitor in parallel with the memristor in the second-order circuit, where symmetrical periodic oscillations and symmetrical chaos emerge either on or near the edge of chaos domains. The oscillation mechanisms of the memristor-based circuits are analyzed via Domains distribution maps, which include the division of locally passive domains, locally active domains, and the edge of chaos domains. Finally, the symmetrical dynamic characteristics are investigated via theory and simulations, including Lyapunov exponents, bifurcation diagrams, and dynamic maps.

Generation of Tunable Stochastic Sequences Using the Insulator-Metal Transition

Probabilistic computing is a paradigm in which data are not represented by stable bits, but rather by the probability of a metastable bit to be in a particular state. The development of this technology has been hindered by the availability of hardware capable of generating stochastic and tunable sequences of "1s" and "0s". The options are currently limited to complex CMOS circuitry and, recently, magnetic tunnel junctions. Here, we demonstrate that metal-insulator transitions can also be used for this purpose. We use an electrical pump/probe protocol and take advantage of the stochastic relaxation dynamics in VO₂ to induce random metallization events. A simple latch circuit converts the metallization sequence into a random stream of 1s and 0s. The resetting pulse in between probes decorrelates successive events, providing a true stochastic digital sequence.

Memristive control of mutual spin Hall nano-oscillator synchronization for neuromorphic computing

Synchronization of large spin Hall nano-oscillator (SHNO) arrays is an appealing approach toward ultrafast non-conventional computing. However, interfacing to the array, tuning its individual oscillators and providing built-in memory units remain substantial challenges. Here, we address these challenges using memristive gating of W/CoFeB/MgO/AlO_x-based SHNOs. In its high resistance state, the memristor modulates the perpendicular magnetic anisotropy at the CoFeB/MgO interface by the applied electric field. In its low resistance state the memristor adds or subtracts current to the SHNO drive. Both electric field and current control affect the SHNO auto-oscillation mode and frequency, allowing us to reversibly turn on/off mutual synchronization in chains of four SHNOs. We also demonstrate that two individually controlled memristors can be used to tune a four-SHNO chain into differently synchronized states. Memristor gating is therefore an efficient approach to input, tune and store the state of SHNO arrays for non-conventional computing models.

A Skin-Inspired Artificial Mechanoreceptor for Tactile Enhancement and Integration

Mechanoreceptors endow humans with the sense of touch by translating the external stimuli into coded spikes, inspiring the rise of artificial mechanoreceptor systems. However, to incorporate slow adaptive receptors-like pressure sensors with artificial neurons remains a challenge. Here we demonstrate an artificial mechanoreceptor by rationally integrating a polypyrrole-based resistive pressure sensor with a volatile NbO_x memristor, to mimic the tactile sensation and perception in natural skin, respectively. The artificial mechanoreceptor enables the tactile sensory coding by converting the external mechanical stimuli into strength-modulated electrical spikes. Also, tactile sensation enhancement is achieved by processing the spike frequency characteristics with the pulse coupled neural network. Furthermore, the artificial mechanoreceptor can integrate signals from parallel sensor channels and encode them into unified electrical spikes, resembling the coding of intensity in tactile neural processing. These results provide simple and efficient strategies for constructing future bio-inspired electronic systems.

Spatiotemporal characterization of the field-induced insulator-to-metal transition

Many correlated systems feature an insulator-to-metal transition that can be triggered by an electric field. Although it is known that metallization takes place through filament formation, the details of how this process initiates and evolves remain elusive. We use in-operando optical reflectivity to capture the growth dynamics of the metallic phase with space and time resolution. We demonstrate that filament formation is triggered by nucleation at hotspots, with a subsequent expansion over several decades in time. By comparing three case studies (VO₂, V₃O₅, and V₂O₃), we identify the resistivity change across the transition as the crucial parameter governing this process. Our results provide a spatiotemporal characterization of volatile resistive switching in Mott insulators, which is important for emerging technologies, such as optoelectronics and neuromorphic computing.