Memristive Switching Research Articles

Optoelectronic Synaptic Memtransistor Based on 2D SnSe/MoS2 van der Waals Heterostructure under UV-Ozone Treatment.

Memristive switching devices with electrically and optically invoked synaptic behaviors show great promise in constructing an artificial biological visual system. Through rational design and integration, 2D materials and their van der Waals (vdW) heterostructures can be applied to realize multifunctional optoelectronic devices. Here, a multifunctional optoelectronic synaptic memtransistor based on a SnSe/MoS2 vdW p-n heterojunction to simulate the human biological visual system is reported. By employing simple mild UV-ozone treatment, the device exhibits reversible resistive switching (RS) behavior with switching ratio up to 103 . The retina-like selective response to different input light wavelengths is activated, as well as programmable multilevel resistance states and long-term synaptic plasticity. Moreover, memory and logic functions analogous to those found in the visual cortex of the brain are performed by controlling the optical and electrical input signals. This work proposes a feasible strategy to modulate RS in vdW heterostructures for memristive devices, which show significant potential for neuromorphic processing.

Effects of quantum recoil forces in resistive switching in memristors

Effects of quantum recoil forces in resistive switching in memristors

Oxide Memristors for ReRAM: Approaches, Characteristics, and Structures

This review focuses on oxide memristors for resistive random access memory (ReRAM). Possi-ble practical implementations of ReRAM and the problem of leakage through neighboring elements in ReRAM are considered. The main types of resistive switching in memristors are briefly described and the main mechanisms of resistive switching are analyzed. The main characteristics of memristors required for ReRAM are also described. Some memristor structures based on oxides of titanium, silicon, tantalum, and hafnium, as well as multilayer oxide structures are analyzed. The current problems in the creation of ReRAM are highlighted.

Effects of Quantum Recoil Forces in Resistive Switching in Memristors

Memristive devices, whose resistance can be controlled by applying a voltage and further retained, are attractive as possible circuit elements for neuromorphic computing. This new type of devices poses a number of both technological and theoretical challenges. Even the physics of the key process of resistive switching, usually associated with formation or breakage of conductive filaments in the memristor, is not completely understood yet. This work proposes a new resistive switching mechanism, which should be important in the thin-filament regime and take place due to the back reaction, or recoil, of quantum charge carriers – independently of the conventional electrostatically-driven ion migration. Since thinnest conductive filaments are in question, which are only several atoms thick and allow for a quasi-ballistic, quantized conductance, we use a mean-field theory and the framework of nonequilibrium Green’s functions to discuss the electron recoil effect for a quantum current through a nanofilament on its geometry and compare it with the transmission probability of charge carriers. Namely, we first study an analytically tractable toy model of a 1D atomic chain, to qualitatively demonstrate the importance of the charge-carrier recoil, and further proceed with a realistic molecular-dynamics simulation of the recoil-driven ion migration along a copper filament and the resulting resistive switching. The results obtained are expected to add to the understanding of resistive switching mechanisms at the nanoscale and to help downscale high-retention memristive devices.

Threshold Switching Memristor-Based Voltage Regulative Circuit

Voltage regulative circuits, which can provide a stable voltage, is an essential part of electronic devices. Memristor, as an emerging device, has drawn attention for voltage regulative circuits design. However, almost all of them is based non-volatile switching memristor, which need complex peripheral circuit for resistance modulation. In this brief, voltage regulative circuits have been designed basing on threshold switching (TS) memristor. The TS device based voltage regulative circuit shows reliable voltage regulation effect for different input signals, such as triangle and sinusoidal voltage input signals. Furthermore, the modulable stable output level has been demonstrated with TS based voltage regulative circuit. This brief provided a new ideal for memristor based voltage regulative circuit realization.

Artificial visual neuron based on threshold switching memristors

The human visual system encodes optical information perceived by photoreceptors in the retina into neural spikes and then processes them by the visual cortex, with high efficiency and low energy consumption. Inspired by this information processing mode, an universal artificial neuron constructed with a resistor (R s) and a threshold switching memristor can realize rate coding by modulating pulse parameters and the resistance of R s. Owing to the absence of an external parallel capacitor, the artificial neuron has minimized chip area. In addition, an artificial visual neuron is proposed by replacing R s in the artificial neuron with a photo-resistor. The oscillation frequency of the artificial visual neuron depends on the distance between the photo-resistor and light, which is fundamental to acquiring depth perception for precise recognition and learning. A visual perception system with the artificial visual neuron can accurately and conceptually emulate the self-regulation process of the speed control system in a driverless automobile. Therefore, the artificial visual neuron can process efficiently sensory data, reduce or eliminate data transfer and conversion at sensor/processor interfaces, and expand its application in the field of artificial intelligence.

Transfer-free graphene passivation of sub 100 nm thin Pt and Pt–Cu electrodes for memristive devices

Memristive switches are among the most promising building blocks for future neuromorphic computing. These devices are based on a complex interplay of redox reactions on the nanoscale. Nanoionic phenomena enable non-linear and low-power resistance transition in ultra-short programming times. However, when not controlled, the same electrochemical reactions can result in device degradation and instability over time. Two-dimensional barriers have been suggested to precisely manipulate the nanoionic processes. But fabrication-friendly integration of these materials in memristive devices is challenging.Here we report on a novel process for graphene passivation of thin platinum and platinum/copper electrodes. We also studied the level of defects of graphene after deposition of selected oxides that are relevant for memristive switching.

Field-Free Current-Induced Switching of L 1 0 - FePt Using Interlayer Exchange Coupling for Neuromorphic Computing

$L{1}_{0}$-phase $\mathrm{Fe}\mathrm{Pt}$ is well known for its unusually robust perpendicular magnetic anisotropy (PMA) properties arising from strong conduction-electron spin-orbit coupling (SOC) with the $\mathrm{Fe}$ orbital moment. The strong PMA enables stable magnetic storage and memory devices with ultrahigh capacity. Meanwhile, SOC is also the premise of the recently discovered spin-orbit-torque (SOT) effect, which opens avenues for possible electrical manipulation of magnetization for $L{1}_{0}$-$\mathrm{Fe}\mathrm{Pt}$. The bulk SOT of the $L{1}_{0}$-$\mathrm{Fe}\mathrm{Pt}$ single layer was discovered recently; this leads to the magnetization of $L{1}_{0}$-$\mathrm{Fe}\mathrm{Pt}$ reversibly switching on itself. However, deterministic SOT switching of bulk perpendicularly magnetized $\mathrm{Fe}\mathrm{Pt}$ magnets relies on an external magnetic field to break the symmetry. Here, the symmetry-breaking issue is resolved by interlayer exchange coupling, where the $\mathrm{Fe}\mathrm{Pt}$ layer is coupled with an in-plane magnetized $\mathrm{Ni}\mathrm{Fe}$ layer through a $\mathrm{Ti}\mathrm{N}$ spacer layer. Furthermore, our device also presents memristive or gradual switching behaviors, even without an external field, offering the potential for constructing spin synapses and spin neurons for neuromorphic computing. An artificial neural network with high accuracy (\ensuremath{\sim}91.17%) is realized based on the constructed synapses and neurons. Our work paves the way for field-free SOT switching of single bulk PMA magnets and their potential applications in neuromorphic computing.

Graphene Oxide-Based Memristive Logic-in-Memory Circuit Enabling Normally-Off Computing.

Memristive logic-in-memory circuits can provide energy- and cost-efficient computing, which is essential for artificial intelligence-based applications in the coming Internet-of-things era. Although memristive logic-in-memory circuits have been previously reported, the logic architecture requiring additional components and the non-uniform switching of memristor have restricted demonstrations to simple gates. Using a nanoscale graphene oxide (GO) nanosheets-based memristor, we demonstrate the feasibility of a non-volatile logic-in-memory circuit that enables normally-off in-memory computing. The memristor based on GO film with an abundance of unusual functional groups exhibited unipolar resistive switching behavior with reliable endurance and retention characteristics, making it suitable for logic-in-memory circuit application. In a state of low resistance, temperature-dependent resistance and I-V characteristics indicated the presence of a metallic Ni filament. Using memristor-aided logic (MAGIC) architecture, we performed NOT and NOR gates experimentally. Additionally, other logic gates such as AND, NAND, and OR were successfully implemented by combining NOT and NOR universal logic gates in a crossbar array. These findings will pave the way for the development of next-generation computer systems beyond the von Neumann architecture, as well as carbon-based nanoelectronics in the future.

Modeling and emulation of artificial nociceptor based on TiO2 threshold switching memristor

Memristors are expected to be further applied in artificial intelligence due to their simple structure and similarity to biological synapses. A model based memristor is developed and optimized to realize an artificial nociceptor, a special receptor that responds to an external stimulus and produces biochemical signals in the body according to the intensity, duration, and number of repetitions of the stimulus. Then the memristors with an ITO/TiOx/TiN structure are presented to imitate nociceptive behaviors including threshold, relaxation, no adaptation, allodynia, and hyperalgesia. The results show that the memristor device we fabricated has a well correspondence with the memristor model. With its excellent synaptic properties and good biocompatibility, TiO2 is expected to show more potential in the fields of artificial intelligence such as electronic skin and humanoid robot.

High‐Temporal‐Resolution Characterization Reveals Outstanding Random Telegraph Noise and the Origin of Dielectric Breakdown in h‐BN Memristors

AbstractMemristor‐based electronic memory have recently started commercialization, although its market size is small (~0.5%). Multiple studies claim their potential for hardware implementation of artificial neural networks, advanced data encryption, and high‐frequency switches for 5G/6G communication. Application aside, the performance and reliability of memristors need to be improved to increase their market size and fit technology standards. Multiple groups propose novel nano‐materials beyond phase‐change, metal‐oxides, and magnetic materials as resistive switching medium (e.g., two‐dimensional, nanowires, perovskites). However, most studies use characterization setups that are blind to critical phenomena in understanding charge transport across the devices. Here an advanced setup with high temporal resolution is used to analyze current noise, dielectric breakdown growth, and ambipolar resistive switching in memristors based on multilayer hexagonal boron nitride (h‐BN), one of the most promising novel nano‐materials for memristive applications. The random telegraph noise in pristine memristors and its evolution as the devices degrade, covering ~7 orders of magnitude in current with consistent observation, is studied. Additionally, an ambipolar switching regime with very low resistance down to 50Ω and its connection with a telegraph behavior with high/low current ratios >100, linked to a thermally‐driven disruption of a metallic nanofilament, is shown.

Amorphous indium–gallium–zinc–oxide memristor arrays for parallel true random number generators

True random number generators (TRNGs) can generate unpredictable binary bitstream by exploiting the intrinsic stochasticity in physical variables. In a threshold switching memristor, the stochastic forming/rupture of conducting pathway has been proved to be a good random source, while further improvement of high randomness and throughput is still a challenge. Here, a crossbar array of amorphous indium–gallium–zinc–oxide (a-IGZO)-based threshold switching memristors was designed for high-throughput TRNGs. The intrinsic stochasticity of Ag conductive filament in IGZO memristor and the stochastic sneak paths in the crossbar array are the two sources of randomness in our TRNGs. In our design, one input pulse train can produce multi-channel random bits, which enables a high scalability for such TRNGs. In addition, the average energy consumption of the TRNGs can be further reduced by increasing the integration scale of the memristors. Such IGZO-based TRNGs are of great significance for security applications.

Stochastic switching of memristors and consideration in circuit simulation

We explore the stochastic switching of oxide-based memristive devices by using the Stanford model for circuit simulation. From measurements, the device-to-device (D2D) and cycle-to-cycle (C2C) statistical variation is extracted. In the low-resistive state (LRS) dispersion by D2D variability is dominant. In the high-resistive state (HRS) C2C dispersion becomes the main source of fluctuation. A statistical procedure for the extraction of parameters of the compact model is presented. Thereby, in a circuit simulation the typical D2D and C2C fluctuations of the current–voltage (I-V) characteristics can be emulated by extracting statistical parameters of key model parameters. The statistical distributions of the parameters are used in a Monte Carlo simulation to reproduce the I-V D2D and C2C dispersions which show a good agreement to the measured curves. The results allow the simulation of the on/off current variation for the design of memory cells or can be used to emulate the synaptic behavior of these devices in artificial neural networks realized by a crossbar array of memristors.

Biodegradable and flexible artificial nociceptor based on Mg/MgO threshold switching memristor

As an important receptor located in the skin, a nociceptor is capable of detecting noxious stimuli and sending warning signals to the central nervous system to avoid tissue damage, thus inspiring the development of artificial nociceptors for electronic receptors. Recently, memristors have attracted increasing attention for developing artificial nociceptors due to the simplicity of the artificial nociceptive system. However, the realization of artificial nociceptors with biocompatibility and biodegradability in a single memristive device remains a challenge. Herein, a fully biocompatible and biodegradable threshold switching (TS) memristor consisting of W/MgO/Mg/W configuration was proposed as an artificial nociceptor. The device showed unidirectional TS characteristics with stable electrical performance under bending conditions. Critical nociceptor behaviors, including threshold, relaxation, no adaptation, allodynia, and hyperalgesia, were successfully demonstrated in the memristive nociceptor. Meanwhile, an optoelectronic nociceptor system was built by the integration of a photoresistor and the memristor. Importantly, the devices transferred on a biodegradable polyvinyl acetate substrate as physically transient electronics could completely dissolve in deionized water, simulating the decomposition of skin necrosis. This study provides a novel route toward developing fully biocompatible and biodegradable artificial nociceptors for promising applications in implantable and wearable electronics and secure bio-integrated systems.

Configurable Crack Wall Conduction in a Complex Oxide

Mobile defects in solid-state materials play a significant role in memristive switching and energy-efficient neuromorphic computation. Techniques for confining and manipulating point defects may have great promise for low-dimensional memories. Here, we report the spontaneous gathering of oxygen vacancies at strain-relaxed crack walls in SrTiO3 thin films grown on DyScO3 substrates as a result of flexoelectricity. We found that electronic conductance at the crack walls was enhanced compared to the crack-free region, by a factor of 104. A switchable asymmetric diode-like feature was also observed, and the mechanism is discussed, based on the electrical migration of oxygen vacancy donors in the background of Sr-deficient acceptors forming n+-n or n-n+ junctions. By tracing the temporal relaxations of surface potential and lattice expansion of a formed region, we determine the diffusivity of mobile defects in crack walls to be 1.4 × 10-16 cm2/s, which is consistent with oxygen vacancy kinetics.

Enhancing memristor fundamentals through instrumental characterization and understanding reliability issues

A memristor is a promising synaptic device for neuromorphic computing. This review article encompasses various instrumental characterization methods which enhance a fundamental understanding of the switching and reliability mechanisms of memristors.

Protons: Critical Species for Resistive Switching in Interface‐Type Memristors (Adv. Electron. Mater. 1/2023)

In article number 2200816, Sundar Kunwar, Aiping Chen, and co-workers demonstrate how protons/moisture-mediated charge trapping/detrapping controls resistive switching in interface-type memristors, which are promising for next generation memory and computing technologies owing to the filamentfree switching, high on/off ratio, low power consumption, and spatial variability.

Memristors based on NdNiO3 nanocrystals film as sensory neurons for neuromorphic computing.

By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO3 (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VoS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.

Soft grafting of DNA over hexagonal copper sulfide for low-power memristor switching

In the present work, a robust, uniform and repeatable room-temperature resistive switching in a Cu/Cu2S/DNA/Au heterojunction is demonstrated.

Emulating Nociceptive Receptor and LIF Neuron Behavior via ZrOx‐based Threshold Switching Memristor

AbstractFor the progress of artificial neural networks, the imitation of multiple biological functions is indispensable for processing more tasks in a complex working environment. Memristors, which possess these advantages such as uniformity, high switching speed, and smaller device scale, are the better candidates compared to conventional complementary metal–oxide–semiconductor (CMOS) technology in artificial neural networks. In this work, an Ag/ZrOx/Pt threshold switching memristor (TSM) is designed to overcome the drawback of the large variation in the non‐volatile filament type memristor. The cycle‐to‐cycle and device‐to‐device variations are 5.6% and 4.9%. This device has mimicked the “nociceptive threshold,” “relaxation,” “no adaptation,” and “sensitization” features for the nociceptor which can prevent the artificial intelligence system from dangers in the external environment. Additionally, with the change in the strength of the external stimulus, the artificial neuron is also built by emulating “all‐or‐nothing,” “threshold‐driven‐spiking,” and “strength‐modulated” characteristics. The proposed threshold‐switching memristor allows the simultaneous emulation of the biological nociceptor and leaky integrate‐and‐fire neuron for the first time, which represents an advance in the bioinspired technology adopted in future artificial neural networks and humanoid robots.