Memristive Switching Research Articles

Defective Engineering Tuning the Analog Switching Linearity and Symmetry of Two‐Terminal Artificial Synapse for Neuromorphic Systems

AbstractNeuromorphic computing inspired by memristors has gained considerable attention due to its low power and easy integration. However, state‐of‐the‐art two‐terminal resistive switching memristors based on conductive filament formation suffer from high variability and poor controllability. As a three‐terminal device operated through electrochemistry and dynamic insertion/extraction of ions, the electrochemical ion synapse demonstrates deterministic control of electron conductivity based on ion doping. But, integrating the electrochemical ion synapse into crossbar arrays will pose higher challenges and lower integration density. Herein, inspired by first‐principles calculations, a two‐terminal bidirectional plasticity electrochemical artificial synapse with integrated lithium polymer electrolyte and polycrystalline tungsten oxide layer is reported. The linearity and stability of the device weight update are greatly improved by adjusting the defect concentration of the polycrystalline WO3 layer. Even after 16 000 write‐read events in the air, its performance remained almost unchanged. Moreover, it has an over‐limit protection mechanism under one‐way stimulation that exceeds the normal range. Based on this excellent stability, the authors designed and successfully simulated the “muscle memory” that the programmatical organization of the nervous system leads to proficiency in specific actions.

The Old Polyoxometalates in New Application as Molecular Resistive Switching Memristors

AbstractThe coming big‐data era has created a huge demand for next‐generation memory technologies with characters of higher data‐storage densities, faster access speeds, lower power consumption and better environmental compatibility. In this field, the design of resistive switching active materials is pivotal but challengeable. Polyoxometalates (POMs) are promising candidates for next‐generation molecular memristors due to their versatile redox characters, excellent electron reservoirs and good compatibility/convenience in microelectronics processing. In this review, five kinds of POM‐based active materials in nonvolatile memories (inorganic POMs, crystalline organic‐inorganic hybrid POMOFs, polymer modified POMs, POM/transition metal oxides composites and the deposition of POM on metal surfaces) were described. The components of POMs active materials, device fabrications, device parameters, and resistive switching mechanisms relative to their structures were summarized. Finally, challenges and future perspectives of POMs‐based memristors were also presented.

Mode-Dependent Adaptive Event-Triggered Control for Stabilization of Markovian Memristor-Based Reaction-Diffusion Neural Networks.

This article focuses on the design of a mode- dependent adaptive event-triggered control (AETC) scheme for the stabilization of Markovian memristor-based reaction-diffusion neural networks (RDNNs). Different from the existing works with completely known transition probabilities, partly unknown transition probabilities (PUTPs) are considered here. The switching conditions and values of memristive connection weights are all correlated with Markovian jumping. A mode-dependent AETC scheme is newly proposed, in which different adaptive event-triggered mechanisms will be applied for different Markovian jumping modes and memristor switching modes. For each given mode, the corresponding event-triggered mechanism can efficiently reduce the number of transmission signals by adaptively adjusting the threshold. Thus, the mode-dependent AETC scheme can effectively save the limited network communication resources for the considered system. Based on the proposed control scheme, a new stabilization criterion is set up for Markovian memristor-based RDNNs with PUTPs. Meanwhile, a memristor-dependent AETC scheme is devised for memristor-based RDNNs. Finally, simulation results are presented to verify the effectiveness and superiority of the analysis results.

Exploring Multifunctionality in MgO‐Based Magnetic Tunnel Junctions with Coexisting Magnetoresistance and Memristive Properties

AbstractMagnetic tunnel junctions (MTJs) and memristors are two key emerging nanotechnologies that attracted significant interest for potential applications at the forefront of the digital revolution, including sensing, data storage, and non‐conventional computation. The co‐integration of these phenomena into a single multifunctional device is an important step toward harnessing the re‐programmability of memristive systems with the high yield and varied functionality of MTJs. This study demonstrates the co‐existence of magnetoresistance and memristive properties on MgO‐based MTJs. These devices show a magnetoresistance with a linear response as a function of a magnetic field and no hysteresis, which are the requirements for good magnetic field sensors, as well as demonstrating a non‐volatile and quasi‐analogue memristive behavior as a function of an applied electrical field down to nanosecond pulses. Furthermore, by doping the oxide barrier, the memristive power consumption is lowered by 20% giving the multi‐functionality of the devices a promising scalability potential. This study also shows that, memristive switching can be reversibly used to completely suppress and recover the spintronic functionalities. These results can pave the way for a seamless co‐integration of memristors and spintronic devices in complex reprogrammable circuits addressing applications such as reprogrammable multifunctional field sensor arrays and neuromorphic computing.

Deep reservoir computing based on self-rectifying memristor synapse for time series prediction

Herein, a self-rectifying resistive switching memristor synapse with a Ta/NbOx/Pt structure was demonstrated for deep reservoir computing (RC). The memristor demonstrated stable nonlinear analog switching characteristics, with a rectification ratio of up to 1.6 × 105, good endurance, and high uniformity. Additionally, the memristor exhibited typical short-term plasticity and dynamic synaptic characteristics. Based on these characteristics, a deep memristor RC system was proposed for time series prediction. The system achieved a low normalized root mean square error (NRMSE) of 0.04 in the time series prediction of the Henon map. Even at 90 °C, deep RC retains good predictive power with an NRMSE of only 0.07. This work provides guidance for efficient deep memristive RC networks to handle more complex future temporal tasks.

Multilevel resistive switching memristor based on silk fibroin/graphene oxide with image reconstruction functionality

Multilevel storage memristors have great potential for use in neuromorphic computing, high-density storage, and the creation of artificial synapses. In this work, we report a multilevel storage memristor based on graphene oxide (GO)/silk fibroin (SF)/GO memristive layer structure. This memristor incorporates binary and ternary switching behaviors in a single device. It was found that the transition of the behavior of the memristor can be transformed between the two resistive switching modes by regulating the compliance current (Icc) applied to the device. Both switching behaviors are stable, repeatable, and nonvolatile. Furthermore, the device shows great potential in simulating synaptic plasticity and is applicable to use in artificial neural networks for digital image recognition as well as image compression and reconstruction. The highest accuracy of recognition of handwritten digital images based on the ternary neural network built by the device is as high as 92.3%. This work highlights GO/SF/GO memristors as promising devices for improving the storage density of memory cells and simplifying the structure of the memristor-based storage system.

3D trigonal FAPbI3‐based multilevel resistive switching nonvolatile memory for artificial neural synapse

AbstractHybrid perovskites have attracted enormous attention in the next generation resistive switching (RS) memristor for the artificial synapses, owing to their ambipolar charge transport, long diffusion length, and tunable visible bandgap. However, the variable switch, limited reproducibility, and poor endurance are the obstacles to the practical application of the perovskite memristors. Herein, we reported a multilevel RS nonvolatile memory based on a 3D trigonal HC(NH2)2PbI3 (α‐FAPbI3) perovskite layer modified by 1‐cyanobutyl‐3‐methylimidazolium chloride ([CNBmim]Cl) and sandwiched between ITO and Au electrodes (Au/[CNBmim]Cl/α‐FAPbI3/SnO2/ITO). In contrast to the bare memristor with failure switching from low resistance state (LRS) to high resistance state (HRS), the memristor device based on the α‐FAPbI3 modified with [CNBmim]Cl (Target device) shows the retention time over 104 s with On/Off ratio (>102) and endurance up to 550 cycles. The stable RS cycle benefits from the accelerated electrons de‐trapping from the reduced defects and fast charge separation in the interface of α‐FAPbI3/electrode, leading to the rupture of conductive filaments and transition of LRS to HRS. As a two‐terminal analog synaptic device, the target device can realize random handwritten digit recognition with an impressive accuracy of 89.3% on the condition of low learning phases (500 training cycles).

A tunable leaky integrate-and-fire neuron based on one neuromorphic transistor and one memristor

Artificial leaky integrate-and-fire (LIF) neurons have attracted significant attention for building brain-like computing and neuromorphic systems. However, previous artificial LIF neurons have primarily focused on implementing integrate-and-fire function, and the function of dendritic modulation has rarely been reported. In this Letter, a tunable artificial LIF neuron based on an IGZO electric-double-layer (EDL) transistor and a TaOx memristor is fabricated, and dendritic modulation is investigated. An IGZO-based EDL transistor with a modulatory terminal is used to realize dendritic nonlinear integration and filtering capability, as well as the tunable neural excitability. An Ag/TaOx/ITO threshold switching memristor mimics the all-or-nothing spiking and threshold switching of the soma. By incorporating these two components in a customized way, such artificial LIF neuron can emulate the key function of biological neuron with rich computational flexibility. Our artificial LIF neurons with rich nonlinear dynamics have great potential to perform more complex tasks in future spiking neuromorphic systems.

An Artificial Olfactory System Based on a Chemi-Memristive Device.

Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Memristive effect in niobium oxide thin films obtained by the pulsed laser deposition

NbOx thin films demonstrating the analog resistive switching characteristics in planar geometry were fabricated by the droplet-free pulsed laser deposition from Nb metal targets. The memristive properties of the films were controlled by tuning the oxygen pressure from 0.7 to 8 Pa and the wavelength of the ablative laser (λ = 248 nm or λ = 532 nm). The stoichiometry of the NbOx films was controlled by the X-ray photoelectron spectroscopy, optical and electrical investigations. Variation of the film thickness from 20 to 50 nm affects the resistive switching characteristics. The 20 nm NbO2 films obtained at the oxygen pressure of 1.3 Pa show a volatile memristive switching with a memory window of ON/OFF resistances ∼ 101 after 500 direct current cycles at an operating voltage of 4.5 V. The 30 nm Nb2O5 films fabricated at the oxygen pressure of 4 Pa, showed a gradual increase in device conductivity with successive positive voltage sweeps with an amplitude of +5 V, which is characteristic of non-volatile memristive switching. Thus, the use of NbOx memristive films of different compositions and crystallinity degree will allow creating neuromorphic systems based on the same material by controlling the memristive behavior by the laser synthesis conditions.

Emulation of auditory senses depending on chaotic dynamics of threshold switching memristor

AbstractEmulating the auditory sense is a significant challenge in terms of both integration and energy consumption for handling complicated spatiotemporal information. Here, we demonstrate how to utilize the chaotic dynamics of a threshold switching memristor, which usually acts as a leaky integrate and fire neuron in the neuromorphic network, to encode the frequency and amplitude in auditory information. We fabricate a Pd/Nb/NbOx/Nb/Pd memristor dominated by the Poole–Frankel conduction mechanism, set its state at the edge of chaos, and stimulate it using periodic perturbations. The memristor's responses to the perturbation frequencies can be categorized into three zones. Two are phase locking with linear phase–frequency relationships, and one has a hyperbolic spike number–frequency relationship. The memristor system also enables intensity coding and tonotopy by modulating the response spikes in either the locked phase or spike number. Based on the emulation of these two features, the memristor system demonstrates sound location and frequency mixing. Our study suggests a novel routine for handling the auditory and visual senses using threshold‐switching memristor arrays to enhance the efficiency of neuromorphic networks.image

Resistive Switching: Physics, Devices and Applications

Resistive Switching: Physics, Devices and Applications

Influence of surface adsorption on MoS2 memtransistor switching kinetics

Sulfur-deficient polycrystalline two-dimensional (2D) molybdenum disulfide (MoS2) memtransistors exhibit gate-tunable memristive switching to implement emerging memory operations and neuromorphic computing paradigms. Grain boundaries and sulfur vacancies are critical for memristive switching; however, the underlying physical mechanisms are not fully understood. Furthermore, the adsorption of water and gaseous species strongly perturbs electronic transport in monolayer MoS2, and little work has been done to explore the influence of surface interactions on defect-related kinetics that produces memristive switching. Here, we study the switching kinetics of back-gated MoS2 memtransistors using current transient measurements in a controlled atmosphere chamber. We observe that adsorbed water molecules lead to suppression of the electronic trap-filling processes concomitant with the resistive switching process, resulting in altered kinetics of the resistive switching. Additionally, using the transient response from “bunched” drain voltage pulse trains performed as a function of temperature, we extract the energy of the affected trap state and find that it places the trap roughly midgap [ET=EC – 0.7 (±0.4) eV]. Our results highlight the importance of controlling for surface interactions that may affect switching kinetics in 2D memtransistors, synaptic transistors, and related memory devices.

Implementation of threshold- and memory-switching memristors based on electrochemical metallization in an identical ferroelectric electrolyte

The use of an identical electrolyte in electrochemical metallization (ECM)-based neuron and synaptic devices has not yet been achieved due to their different resistive-switching characteristics. Herein, we describe ECM devices comprising the same ferroelectric PbZr0.52Ti0.48O3 (PZT) electrolyte, which can sustain both neuron and synaptic behavior depending on the identity of the active electrode. The Ag/PZT/La0.8Sr0.2MnO3 (LSMO) threshold switching memristor shows abrupt and volatile resistive switching characteristics, which lead to neuron devices with stochastic integration-and-fire behavior, auto-recovery, and rapid operation. In contrast, the Ni/PZT/LSMO memory switching memristor exhibits gradual, non-volatile resistive switching behavior, which leads to synaptic devices with a high on/off ratio, low on-state current, low variability, and spike-timing-dependent plasticity (STDP). The divergent behavior of the ECM devices is attributed to greater control of cation migration through the ultrathin ferroelectric PZT. Thus, ECM devices with an identical ferroelectric electrolyte offer promise as essential building blocks in the construction of high-performance neuromorphic computing systems.

Nano t-Se Peninsulas Embedded in Natively Oxidized 2D TiSe2 Enable Uniform and Fast Memristive Switching.

Memristive devices, regardless of their potential applications in memory and computing scenarios, still suffer from large cycle-to-cycle and device-to-device variations due to the stochastic growth of conductive filaments (CFs). In this work, we fabricated a crossbar memristor using the 2D TiSe2 material and then oxidized it into TiO2 in the atmosphere at a moderate temperature. Such a mild oxidation approach fails to evaporate all Se into the air, and after further annealing using thermal or electrical stimulations, the remnant Se atoms gather near the interfaces and grow into nanosized crystals with relatively high conductivity. The resulting peninsula-shaped nanocrystals distort the electric field, forcing CFs to grow on them, which could largely confine the location and length of CFs. As a result, this two-terminal TiSe2/TiO2/TiSe2 device exhibits excellent resistive switching performance with a fairly low threshold voltage (Vset < 0.8 V, Vreset > 0.55 V) and high cycle-to-cycle consistency, enabling resistive switching at narrow operating variations, e.g., 500 ± 48 and 845 ± 39 mV. Our work offers a new approach to minimize the cycle-to-cycle stochasticity of the memristive device, paving the way for its applications in data storage and brain-inspired computing.

Thermal design engineering for improving the variation of memristor threshold

Memristor-based neural components and circuits are the key to building efficient brain-like computing systems, but their development is restricted by their unstable electrical properties of memristive artificial neurons. In this work, three thermal design schemes to regulate the electrical characteristics of threshold switching memristor were proposed, such as the thermal conductivity of the device electrode, the different substrate and local electrode. First, it was found that the lower the thermal conductivity of electrode, the lower the threshold variation of the device. Secondly, the smaller the thermal conductivity of the memristor substrate, the more stable the threshold. Thirdly, decreasing the electrode radius of the memristor from the original 50 nm to 10 nm will decrease the variation of threshold voltage by 27%. Additionally, due to the reduction of thermal dissipation, the proposed thermal design engineering also can lower the threshold voltage and power consumption. Those results on improving the threshold variation of memristor provide a stepping stone for the development of artificial neurons and neuromorphic computing systems.

A 2.22 Mb/s True Random Number Generator Based on a GeTex Ovonic Threshold Switching Memristor

True random number generators (TRNGs) based on threshold switching memristors emerge as a building block for secure electronics. However, the throughputs reported in previous studies have a stark gap with the requirements of practical applications (>1 Mb/s). Here, we implement a high-speed TRNG with a GeTe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> ovonic threshold switching (OTS) memristor. The TRNG throughput reaches 2.22 Mb/s for a single cell, which is 2.2 times faster than the prior state-of-the-art threshold-switching-based TRNG. In addition, the TRNG endurance of 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> bits was achieved, and the random bits passed 12 tests in the National Institute of Standards and Technology statistical test suite. Our results demonstrated that the OTS-based TRNG could provide a high–throughput and highly secure solution for edge applications.

Direct Observation of Oxygen Ion Dynamics in a WO3‐x based Second‐Order Memristor with Dendritic Integration Functions

AbstractDirect observation of oxygen dynamics in an oxide‐based second‐order memristor can provide the valid evidence to clarify the memristive mechanism, however, which is still limited for now. In this study, the migration and diffusion of oxygen ions in the region of Pt/WO3‐x Schottky interface are observed in the WO3‐x second‐order memristor by using the technique of in situ transmission electron microscopy (TEM) and the electron energy loss spectroscopy. Interestingly, the coexistence of memristive and memcapacitive switching can be implemented in this memristor. Combined with the analysis of depth‐profile X‐ray photoelectron spectroscopy (XPS), an interface‐barrier‐modulation second‐order memristive model is proposed based on the above results. Notably, temporally correlative oxygen dynamics in the memristor offers the platform to integrate signals from multiple inputs, enabling the realization of the dendritic functions of synchronous and asynchronous integration for the application of logic operations with fault‐tolerance capability and associative learning. These findings provide the experimental evidence to in‐depth understanding of oxygen dynamics and switching mechanism in second‐order memristor, which can support the optimization of memristive performance and the achievement of biorealistic synaptic functions.

Resistive switching and artificial synaptic performances of memristor based on low-dimensional bismuth halide perovskites

Resistive switching and artificial synaptic performances of memristor based on low-dimensional bismuth halide perovskites

Heterosynaptic MoS2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics.

Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.