Ultra-low-energy three-dimensional oxide-based electronic synapses for implementation of robust high-accuracy neuromorphic computation systems

Strain-mediated multistate skyrmion for neuron devices

Magnetic skyrmions are potential candidates for neuromorphic computing because of their inherent topological stability, low drive current density and nanoscale size. However, an artificial neuron device based on current-driven skyrmion motion cannot satisfy the requirement of energy efficiency and integration density due to hundreds of millions of interconnected neurons and synapses present in the deep networks. Here, we present a compact and energy efficient skyrmion-based artificial neuron consisting of ferromagnetic/heavy metal/ferroelectric layers which uses strain-mediated voltage manipulation of skyrmion states to mimic the Integrate-and-Fire (IF) function of biological neurons. By implementation of a spiking neural network (SNN) based on the proposed skyrmionic neuronal devices, it can achieve a high accuracy of 95.08% on a modified National Institute of Standards and Technology (MNIST) handwritten digit dataset, as well as a low power consumption of ∼46.8 fJ per epoch per neuron. The present work suggests a novel way to realize energy-efficient and high-density neuromorphic computing.

Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures

Hybrid halide perovskites are attractive candidates for resistive switching memories in neuromorphic computing applications due to their mixed ionic-electronic conductivity. Moreover, their exceptional optoelectronic characteristics make them effective as semiconductors in photovoltaics, opening perspectives for self-powered memory elements. These devices, however, remain unexploited, which is related to the variability in their switching characteristics, weak endurance, and retention, which limit their performance and practical use. To address this challenge, we applied low-dimensional perovskite capping layers onto 3D mixed halide perovskites using two perfluoroarene organic cations, namely (perfluorobenzyl)ammonium and (perfluoro-1,4-phenylene)dimethylammonium iodide, forming Ruddlesden-Popper and Dion-Jacobson 2D perovskite phases, respectively. The corresponding mixed-dimensional perovskite heterostructures were used to fabricate resistive switching memories based on perovskite solar cell architectures, showing that the devices based on perfluoroarene heterostructures exhibited enhanced performance and stability in inert and ambient air atmosphere. This opens perspectives for multidimensional perovskite materials in durable self-powered memory elements in the future.

Silver Nanowire Networks with Moisture-Enhanced Learning Ability

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

Double-Forming Mechanism of TaOx-Based Resistive Memory Device and Its Synaptic Applications

The bipolar resistive switching properties of Pt/TaOx/InOx/ITO-resistive random-access memory devices under DC and pulse measurement conditions are explored in this work. Transmission electron microscopy and X-ray photoelectron spectroscopy were used to confirm the structure and chemical compositions of the devices. A unique two-step forming process referred to as the double-forming phenomenon and self-compliance characteristics are demonstrated under a DC sweep. A model based on oxygen vacancy migration is proposed to explain its conduction mechanism. Varying reset voltages and compliance currents were applied to evaluate multilevel cell characteristics. Furthermore, pulses were applied to the devices to demonstrate the neuromorphic system's application via testing potentiation, depression, spike-timing-dependent plasticity, and spike-rate-dependent plasticity.

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.

Memristor-Based Signal Processing for Compressed Sensing

With the rapid progress of artificial intelligence, various perception networks were constructed to enable Internet of Things (IoT) applications, thereby imposing formidable challenges to communication bandwidth and information security. Memristors, which exhibit powerful analog computing capabilities, emerged as a promising solution expected to address these challenges by enabling the development of the next-generation high-speed digital compressed sensing (CS) technologies for edge computing. However, the mechanisms and fundamental properties of memristors for achieving CS remain unclear, and the underlying principles for selecting different implementation methods based on various application scenarios have yet to be elucidated. A comprehensive overview of memristor-based CS techniques is currently lacking. In this article, we systematically presented CS requirements on device performance and hardware implementation. The relevant models were analyzed and discussed from the mechanism level to elaborate the memristor CS system scientifically. In addition, the method of deploying CS hardware using the powerful signal processing capabilities and unique performance of memristors was further reviewed. Subsequently, the potential of memristors in all-in-one compression and encryption was anticipated. Finally, existing challenges and future outlooks for memristor-based CS systems were discussed.

Realizing Electronic Synapses by Defect Engineering in Polycrystalline Two-Dimensional MoS2 for Neuromorphic Computing

Neuromorphic computing based on two-dimensional transition-metal dichalcogenides (2D TMDs) has attracted significant attention recently due to their extraordinary properties generated by the atomic-thick layered structure. This study presents sulfur-defect-assisted MoS₂ artificial synaptic devices fabricated by a simple sputtering process, followed by a precise sulfur (S) vacancy-engineering process. While the as-sputtered MoS₂ film does not show synaptic behavior, the S vacancy-controlled MoS₂ film exhibits excellent synapse with remarkable nonvolatile memory characteristics such as a high switching ratio (∼10³), a large memory window, and long retention time (∼10⁴ s) in addition to synaptic functions such as paired-pulse facilitation (PPF) and long-term potentiation (LTP)/depression (LTD). The synaptic device working mechanism of Schottky barrier height modulation by redistributing S vacancies was systemically analyzed by electrical, physical, and microscopy characterizations. The presented MoS₂ synaptic device, based on the precise defect engineering of sputtered MoS₂, is a facile, low-cost, complementary metal-oxide semiconductor (CMOS)-compatible, and scalable method and provides a procedural guideline for the design of practical 2D TMD-based neuromorphic computing.

Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing

The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.

Metal-Organic Frameworks-Based Memristors: Materials, Devices, and Applications

Facing the explosive growth of data, a number of new micro-nano devices with simple structure, low power consumption, and size scalability have emerged in recent years, such as neuromorphic computing based on memristor. The selection of resistive switching layer materials is extremely important for fabricating of high performance memristors. As an organic-inorganic hybrid material, metal-organic frameworks (MOFs) have the advantages of both inorganic and organic materials, which makes the memristors using it as a resistive switching layer show the characteristics of fast erasing speed, outstanding cycling stability, conspicuous mechanical flexibility, good biocompatibility, etc. Herein, the recent advances of MOFs-based memristors in materials, devices, and applications are summarized, especially the potential applications of MOFs-based memristors in data storage and neuromorphic computing. There also are discussions and analyses of the challenges of the current research to provide valuable insights for the development of MOFs-based memristors.

Threshold Switching in Forming-Free Anodic Memristors Grown on Hf-Nb Combinatorial Thin-Film Alloys

The development of novel materials with coexisting volatile threshold and non-volatile memristive switching is crucial for neuromorphic applications. Hence, the aim of this work was to investigate the memristive properties of oxides in a Hf-Nb thin-film combinatorial system deposited by sputtering on Si substrates. The active layer was grown anodically on each Hf-Nb alloy from the library, whereas Pt electrodes were deposited as the top electrodes. The devices grown on Hf-45 at.% Nb alloys showed improved memristive performances reaching resistive state ratios up to a few orders of magnitude and achieving multi-level switching behavior while consuming low power in comparison with memristors grown on pure metals. The coexistence of threshold and resistive switching is dependent upon the current compliance regime applied during memristive studies. Such behaviors were explained by the structure of the mixed oxides investigated by TEM and XPS. The mixed oxides, with HfO₂ crystallites embedded in quasi amorphous and stoichiometrically non-uniform Nb oxide regions, were found to be favorable for the formation of conductive filaments as a necessary step toward memristive behavior. Finally, metal-insulator-metal structures grown on the respective alloys can be considered as relevant candidates for the future fabrication of anodic high-density in-memory computing systems for neuromorphic applications.

Ion-Movement-Based Synaptic Device for Brain-Inspired Computing

As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.

Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy-Efficient Image Learning

The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiO_x memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (P_Act ) from 0 to 1.0, and can even modulate the degree of the P_Act change. A drop-connected algorithm based on the P_Act is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all-to-all connected network while exhibiting a high recognition accuracy of ≈93 %.

High-Performing Self-Powered Photosensing and Reconfigurable Pyro-photoelectric Memory with Ferroelectric Hafnium Oxide

With highly diverse multifunctional properties, hafnium oxide (HfO₂ ) has attracted considerable attention not only because of its potential to address fundamental questions about material behaviors, but also its potential for applied perspectives like ferroelectric memory, transistors, and pyroelectric sensors. However, effective harvesting of the pyro-photoelectric effect of HfO₂ to develop high-performing self-biased photosensors and electric writable and optical readable memory has yet to be developed. Here, a proof-of-concept HfO₂ -based self-powered and ultrafast (response time ≈ 60 µs) infrared pyroelectric sensor with a responsivity of up to 68 µA W^-1 is developed. In particular, temporal infrared light illumination induced surface heating and, in turn, change in spontaneous polarization are attributed to robust pyro-photocurrent generation. Further, controllable suspension and reestablishment of the self-biased pyro-photocurrent response with a short electric pulse are demonstrated, which offers a conceptually new kind of photoreadable memory. Potentially, the novel approach opens a new avenue for designing on-demand pyro-phototronic response over a desired area and offers the opportunity to utilize it for various applications, including memory storage, neuromorphic vision sensors, classification, and emergency alert systems.

Achieving complementary resistive switching and multi-bit storage goals by modulating the dual-ion reaction through supercritical fluid-assisted ammoniation

Complementary resistive switching (CRS) is a core requirement in memristor crossbar array construction for neuromorphic computing in view of its capability to avoid the sneak path current. However, previous approaches for implementing CRS are generally based on a complex device structure design and fabrication process or a meticulous current-limiting measurement procedure. In this study, a supercritical fluid-assisted ammoniation (SFA) process is reported to achieve CRS in a single device by endowing the original ordinary switching materials with dual-ion operation. In addition to self-compliant CRS behavior, a multi-bit storage function has also been achieved through the SFA process accompanied by superior retention and reliability. These substantial evolved resistive phenomena are elucidated attentively by our chemical reaction model and physical mechanism model corroborated by the material analysis and current conduction fitting analysis results. The findings in this research present the most efficient way to achieve CRS through only one chemical procedure with significantly improved device performance. Moreover, the supercritical fluid approach envisions tremendous possibilities for further development of materials and electric devices by a low-temperature process, with semiconductor fabrication compatibility and environmental friendliness.

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal-oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

Giant Ferroelectric Resistance Switching Controlled by a Modulatory Terminal for Low-Power Neuromorphic In-Memory Computing

Ferroelectrics have been demonstrated as excellent building blocks for high-performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in-memory computing. Here, it is reported that the emerging van der Waals ferroelectric α-In₂ Se₃ can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance-switching ratio of heterosynaptic memristors above 10³ , which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α-In₂ Se₃ channel is responsible for the resistance switching at various paired terminals. The third terminal of α-In₂ Se₃ memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read-energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α-In₂ Se₃ neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy-efficient, brain-inspired computing systems and logic-in-memory computers.

Modeling-Based Design of Memristive Devices for Brain-Inspired Computing

Resistive switching random access memory (RRAM) has emerged for non-volatile memory application with the features of simple structure, low cost, high density, high speed, low power, and CMOS compatibility. In recent years, RRAM technology has made significant progress in brain-inspired computing paradigms by exploiting its unique physical characteristics, which attempts to eliminate the energy-intensive and time-consuming data transfer between the processing unit and the memory unit. The design of RRAM-based computing paradigms, however, requires a detailed description of the dominant physical effects correlated with the resistive switching processes to realize the interaction and optimization between devices and algorithms or architectures. This work provides an overview of the current progress on device-level resistive switching behaviors with detailed insights into the physical effects in the resistive switching layer and the multifunctional assistant layer. Then the circuit-level physics-based compact models will be reviewed in terms of typical binary RRAM and the emerging analog synaptic RRAM, which act as an interface between the device and circuit design. After that, the interaction between device and system performances will finally be addressed by reviewing the specific applications of brain-inspired computing systems including neuromorphic computing, in-memory logic, and stochastic computing.

Transparent HfO x -based memristor with robust flexibility and synapse characteristics by interfacial control of oxygen vacancies movement

Hafnium oxides (HfO _x ) based flexible memristors were fabricated on polyethylene naphtholate (PEN) substrates to simulate a variety of bio-synapse functions. By optimizing the manufacturing conditions of electrode and active films, it is proved that the TiN/HfO _x /W/ITO/PEN bilayer device has robust flexibility and can still be modulated after 2000 times of bending. The memristor device exhibits better symmetrical and linear characteristics with excellent uniformity at lower programming power consumption (∼38 μW). In addition, the essential synaptic behaviors have further been achieved in the devices, including the transition from short-term plasticity to long-term plasticity and spike time-dependent plasticity. Through the analysis of I-V curves and XPS data, a switching mechanism based on HfO _x /W interface boundary drift is constructed. It is revealed that the redox reaction caused by W intercalation can effectively regulate the content of oxygen vacancy in HfO _x . At the same time, bias-induced interfacial reactions will regulate the movement of oxygen vacancies, which emulates bio-synapse functions and improves the electrical properties of the device.

Antiferromagnetic skyrmion repulsion based artificial neuron device

Magnetic skyrmions are potential candidates for neuromorphic computing due to their inherent topologically stable particle-like behavior, low driving current density, and nanoscale size. Antiferromagnetic skyrmions are favored as they can be driven parallel to in-plane electrical currents as opposed to ferromagnetic skyrmions which exhibit the skyrmion Hall effect and eventually cause their annihilation at the edge of nanotracks. In this paper, an antiferromagnetic skyrmion based artificial neuron device consisting of a magnetic anisotropy barrier on a nanotrack is proposed. It exploits inter-skyrmion repulsion, mimicking the integrate-fire (IF) functionality of a biological neuron. The device threshold represented by the maximum number of skyrmions that can be pinned by the barrier can be tuned based on the particular current density employed on the nanotrack. The corresponding neuron spiking event occurs when a skyrmion overcomes the barrier. By raising the device threshold, lowering the barrier width and height, the operating current density of the device can be decreased to further enhance its energy efficiency. The proposed device paves the way for developing energy-efficient neuromorphic computing in antiferromagnetic spintronics.

Evolution of Bio-Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Artificial synapses (ASs) are electronic devices emulating important functions of biological synapses, which are essential building blocks of artificial neuromorphic networks for brain-inspired computing. A human brain consists of several quadrillion synapses for information storage and processing, and massively parallel computation. Neuromorphic systems require ASs to mimic biological synaptic functions, such as paired-pulse facilitation, short-term potentiation, long-term potentiation, spatiotemporally-correlated signal processing, and spike-timing-dependent plasticity, etc. Feature size and energy consumption of ASs need to be minimized for high-density energy-efficient integration. This work reviews recent progress on ASs. First, synaptic plasticity and functional emulation are introduced, and then synaptic electronic devices for neuromorphic computing systems are discussed. Recent advances in flexible artificial synapses for artificial sensory nerves are also briefly introduced. Finally, challenges and opportunities in the field are discussed.

Enhancing LiAlOX synaptic performance by reducing the Schottky barrier height for deep neural network applications

Although good performance has been reported in shallow neural networks, the application of memristor synapses towards realistic deep neural networks has met more stringent requirements on the synapse properties, particularly the high precision and linearity of the synaptic analog weight tuning. In this study, a LiAlOX memristor synapse was fabricated and optimized to address these demands. By delicately tuning the initial conductance states, 120-level continuously adjustable conductance states were obtained and the nonlinearity factor was substantially reduced from 8.96 to 0.83. The significant enhancements were attributed to the reduced Schottky barrier height (SBH) between the filament tip and the electrode, which was estimated from the measured I-V curves. Furthermore, a deep neural network for realistic action recognition task was constructed, and the recognition accuracy was found to be increased from 15.1% to 91.4% on the Weizmann video dataset by adopting the above-described device optimization method.

A Robust and Low-Power Bismuth Doped Tin Oxide Memristor Derived from Coaxial Conductive Filaments

Memristor, processing data storage and logic operation all-in-one, is an advanced configuration for next generation computer. In this work, a bismuth doped tin oxide (Bi:SnO₂ ) memristor with ITO/Bi:SnO₂ /TiN structure has been fabricated. Observing from transmission electron microscope (TEM) for the Bi:SnO₂ device, it is found that the bismuth atoms surround the surface of SnO₂ crystals to form the coaxial Bi conductive filament. The self-compliance current, switching voltage and operating current of Bi:SnO₂ memristor are remarkably smaller than that of ITO/SnO₂ /TiN device. With the content of 4.8% Bi doping, the SET operating power of doped device is 16 µW for ITO/Bi:SnO₂ /TiN memory cell of 0.4 × 0.4 µm² , which is cut down by two orders of magnitude. Hence, the findings in this study suggest that Bi:SnO₂ memristors hold significant potential for application in low power memory and broadening the understanding of existing resistive switching (RS) mechanism.

Lead-Free Dual-Phase Halide Perovskites for Preconditioned Conducting-Bridge Memory

Organometallic and all-inorganic halide perovskites (HPs) have recently emerged as promising candidate materials for resistive switching (RS) nonvolatile memory due to their current-voltage hysteresis caused by fast ion migration. Lead-free and all-inorganic HPs have been researched for non-toxic and environmentally friendly RS memory devices. However, only HP-based devices with electrochemically active top electrode (TE) exhibit ultra-low operating voltages and high on/off ratio RS properties. The active TE easily reacts to halide ions in HP films, and the devices have a low device durability. Herein, RS memory devices based on an air-stable lead-free all-inorganic dual-phase HP (AgBi₂ I₇ -Cs₃ Bi₂ I₉ ) are successfully fabricated with inert metal electrodes. The devices with Au TE show filamentary RS behavior by conducting-bridge involving Ag cations in HPs with ultra-low operating voltages (<0.15 V), high on/off ratio (>10⁷ ), multilevel data storage, and long retention times (>5 × 10⁴ s). The use of a closed-loop pulse switching method improves reversible RS properties up to 10³ cycles with high on/off ratio above 10⁶ . With an extremely small bending radius of 1 mm, the devices are operable with reasonable RS characteristics. This work provides a promising material strategy for lead-free all-inorganic HP-based nonvolatile memory devices for practical applications.

Biological receptor-inspired flexible artificial synapse based on ionic dynamics

The memristor has been regarded as a promising candidate for constructing a neuromorphic computing platform that is capable of confronting the bottleneck of the traditional von Neumann architecture. Here, inspired by the working mechanism of the G-protein-linked receptor of biological cells, a novel double-layer memristive device with reduced graphene oxide (rGO) nanosheets covered by chitosan (an ionic conductive polymer) as the channel material is constructed. The protons in chitosan and the functional groups in rGO nanosheets imitate the functions of the ligands and receptors of biological cells, respectively. Smooth changes in the response current depending on the historical applied voltages are observed, offering a promising pathway toward biorealistic synaptic emulation. The memristive behavior is mainly a result of the interaction between protons provided by chitosan and the defects and functional groups in the rGO nanosheets. The channel current is due to the hopping of protons through functional groups and is limited by the traps in the rGO nanosheets. The transition from short-term to long-term potentiation is achieved, and learning-forgetting behaviors of the memristor mimicking those of the human brain are demonstrated. Overall, the bioinspired memristor-type artificial synaptic device shows great potential in neuromorphic networks.

Nonlinear Characteristics of Complementary Resistive Switching in HfAlOx-Based Memristor for High-Density Cross-Point Array Structure

In this work, we present the nonlinear current–voltage (I–V) characteristics of a complementary resistive switching (CRS)-like curve from a HfAlOx-based memristor, used to implement a high-density cross-point array. A Pt/HfAlOx/TiN device has lower on-current and larger selectivity compared to Pt/HfO2/TiN or Pt/Al2O3/TiN devices. It has been shown that the on-current and first reset peak current after the forming process are crucial in obtaining a CRS-like curve. We demonstrate transient CRS-like characteristics with high nonlinearity under pulse response for practical applications. Finally, after finding the optimal conditions for high selectivity, the calculated read margin proves that a Pt/HfAlOx/TiN device with a CRS-like curve is most suitable for use in a high-density cross-point array. Our results suggest that the built-in selector properties in a Pt/HfAlOx/TiN single layer device offer considerable potential in terms of the simplicity of the processes involved in the cross-point structure.

Variable-temperature activation energy extraction to clarify the physical and chemical mechanisms of the resistive switching process

This study investigates the physical and chemical mechanisms during the resistive switching process by means of obtaining the activation energy in the reaction procedure. From the electrochemical and electrical measurement analysis results of HfO2-based resistive random access memory (RRAM), it can be observed that the chemical reaction during the reset process is consistent with the first-order reaction law. The activation energy, Ea, is determined from the reaction rate constant k under a varying-temperature environment in the reset process. The whole reset chemical reaction process can be divided into five phases involving N-O bond breaking, O-O bond breaking and triple-step oxygen ion migration. The methodology of the activation energy determination carried out in this study showcases a distinct approach to elucidate the resistive switching mechanism of RRAM and offers insight into RRAM design for future potential application.

The Design of 3D-Interface Architecture in an Ultralow-Power, Electrospun Single-Fiber Synaptic Transistor for Neuromorphic Computing

Synaptic electronics is a new technology for developing functional electronic devices that can mimic the structure and functions of biological counterparts. It has broad application prospects in wearable computing chips, human-machine interfaces, and neuron prostheses. These types of applications require synaptic devices with ultralow energy consumption as the effective energy supply for wearable electronics, which is still very difficult. Here, artificial synapse emulation is demonstrated by solid-ion gated organic field-effect transistors (OFETs) with a 3D-interface conducting channel for ultralow-power synaptic simulation. The basic features of the artificial synapse, excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and high-pass filtering, are successfully realized. Furthermore, the single-fiber based artificial synapse can be operated by an ultralow presynaptic spike down to -0.5 mV with an ultralow reading voltage at -0.1 mV due to the large contact surface between the ionic electrolyte and fiber-like semiconducting channel. Therefore, the ultralow energy consumption at one spike of the artificial synapse can be realized as low as ≈3.9 fJ, which provides great potential in a low-power integrated synaptic circuit.

An indirect way to achieve comprehensive performance improvement of resistive memory: when hafnium meets ITO in an electrode

Emerging resistive random access memory has attracted extensive research enthusiasm. In this study, an indirect way to improve resistive random access memory (RRAM) comprehensive performance through electrode material re-design without intensive switching layer engineering is presented by adopting a hafnium-indium-tin-oxide composite. Working parameters of the device can be effectively improved: not only are low operation power consumption and high working stability achieved, but the memory window is significantly enlarged, accompanied by an automatic self-current-compliance function. The correlation between hafnium incorporation and performance improvements and the corresponding current conduction mechanisms have been thoroughly investigated to clarify the resistive switching behavior and to explain the oxygen absorption buffer effect. The hafnium atom, with large atomic radius, is surrounded by soft electron clouds and has high chemical activity to attract oxygen ions. It facilitates the accumulation of more oxygen ions around the interface of the top electrode and the resistive switching layer, leading to lower current and Schottky conduction. This study presents an important strategy for designing and developing electrode materials to improve the characteristics of RRAM and offers an indirect method to modify device working behaviors, also unveiling a promising prospect for its potential future application in low-power information storage and calculation technology.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

Three-Dimensional (3D) Vertical Resistive Random-Access Memory (VRRAM) Synapses for Neural Network Systems

Memristor devices are generally suitable for incorporation in neuromorphic systems as synapses because they can be integrated into crossbar array circuits with high area efficiency. In the case of a two-dimensional (2D) crossbar array, however, the size of the array is proportional to the neural network’s depth and the number of its input and output nodes. This means that a 2D crossbar array is not suitable for a deep neural network. On the other hand, synapses that use a memristor with a 3D structure are suitable for implementing a neuromorphic chip for a multi-layered neural network. In this study, we propose a new optimization method for machine learning weight changes that considers the structural characteristics of a 3D vertical resistive random-access memory (VRRAM) structure for the first time. The newly proposed synapse operating principle of the 3D VRRAM structure can simplify the complexity of a neuron circuit. This study investigates the operating principle of 3D VRRAM synapses with comb-shaped word lines and demonstrates that the proposed 3D VRRAM structure will be a promising solution for a high-density neural network hardware system.

Light Driven Active Transition of Switching Modes in Homogeneous Oxides/Graphene Heterostructure

Depending on the mobile species involved in the resistive switching process, redox random access memories and conductive bridge random access memories are widely studied with distinct switching mechanisms. Although the two resistance switching types have faithfully proved to be electrochemically linked in metal oxide-based memristive devices, the corresponding photo-induced transition has not yet been realized. Here, a photo-induced transition through the integration of a graphene layer into a titanium oxide-based memory device is demonstrated. Coupled with Raman mapping and an electron energy loss spectroscopy technique, the photo-induced interaction at the heterostructure of graphene/titanium oxide are considered to dominate the transition process. Moreover, a negative differential resistance effect is observed by controlling the applied voltage, which can be credited to the saturation of trap centers (oxygen vacancies) and the increase of interfacial barrier at the graphene/titanium oxide heterojunction.

Stimulated Ionic Telegraph Noise in Filamentary Memristive Devices

Random telegraph noise is a widely investigated phenomenon affecting the reliability of the reading operation of the class of memristive devices whose operation relies on formation and dissolution of conductive filaments. The trap and the release of electrons into and from defects surrounding the filament produce current fluctuations at low read voltages. In this work, telegraphic resistance variations are intentionally stimulated through pulse trains in HfO₂-based memristive devices. The stimulated noise results from the re-arrangement of ionic defects constituting the filament responsible for the switching. Therefore, the stimulated noise has an ionic origin in contrast to the electronic nature of conventional telegraph noise. The stimulated noise is interpreted as raising from a dynamic equilibrium establishing from the tendencies of ionic drift and diffusion acting on the edges of conductive filament. We present a model that accounts for the observed increase of noise amplitude with the average device resistance. This work provides the demonstration and the physical foundation for the intentional stimulation of ionic telegraph noise which, on one hand, affects the programming operations performed with trains of identical pulses, as for neuromorphic computing, and on the other hand, it can open opportunities for applications relying on stochastic processes in nanoscaled devices.

Configurable multi-state non-volatile memory behaviors in Ti3C2 nanosheets

MXenes have drawn considerable attention in both academia and industry due to their attractive properties, such as a combination of metallic conductivity and surface hydrophilicity. However, to the best of our knowledge, the potential use of MXenes in non-volatile resistive random access memories (RRAMs) has rarely been reported. In this paper, we first demonstrated a RRAM device with MXene (Ti3C2) as the active component. The Ti3C2-based RRAM exhibited typical bipolar switching behavior, long retention characteristics, low SET voltage, good mechanical stability and excellent reliability. By adjusting different compliance currents in the SET process, multi-state information storage was achieved. The charge trapping assisting hopping process is considered to be the main mechanism of resistive switching for this fabricated Ti3C2-based RRAM, which was verified by conductive atomic force microscopy (C-AFM) and Kelvin probe force microscopy (KPFM). Moreover, this flexible Ti3C2-based RRAM, with good mechanical stability and long retention properties, was successfully fabricated on a plastic substrate. Ti3C2-based RRAMs may open the door to additional applications and functionalities, with high potential for application in flexible electronics.

Neuronal dynamics in HfOx/AlOy-based homeothermic synaptic memristors with low-power and homogeneous resistive switching

We studied the pseudo-homeothermic synaptic behaviors by integrating complimentary metal-oxide-semiconductor-compatible materials (hafnium oxide, aluminum oxide, and silicon substrate). A wide range of temperatures, from 25 °C up to 145 °C, in neuronal dynamics was achieved owing to the homeothermic properties and the possibility of spike-induced synaptic behaviors was demonstrated, both presenting critical milestones for the use of emerging memristor-type neuromorphic computing systems in the near future. Biological synaptic behaviors, such as long-term potentiation, long-term depression, and spike-timing-dependent plasticity, are developed systematically, and comprehensive neural network analysis is used for temperature changes and to conform spike-induced neuronal dynamics, providing a new research regime of neurocomputing for potentially harsh environments to overcome the self-heating issue in neuromorphic chips.

An electronic synaptic device based on HfO2TiOx bilayer structure memristor with self-compliance and deep-RESET characteristics

We reported on a Ti/HfO₂/TiO_x/Pt memristor with self-compliance, deep-RESET characteristics and excellent switching performance, including ultrafast program/erase speed (10 ns), a large memory window (10³) and good pulse endurance (10⁷ cycles). The self-compliance and deep-RESET characteristics are beneficial for protecting the device from permanent breakdown in both SET and RESET processes especially under the pulse operation mode. In addition to bistable state switching, we also achieved multiple or even continuous conductance state switching under a DC sweep and a pulse-train operation mode in the Ti/HfO₂/TiO_x/Pt memristor, which can be seen as a substitution of a biological synapse. The capability of continuous modulation conductance (synaptic weight) in the Ti/HfO₂/TiO_x/Pt memristor was investigated and the potentiation and depression characteristics of the synaptic weight could be precisely tuned by the number or amplitude of the input pulse-train. Moreover, clear experimental evidence of short-term plasticity (STP) and long-term plasticity (LTP) in a single memristor was also demonstrated. Increasing the pulse amplitude or width, or decreasing the interval of two adjacent pulses of the input pulse-train resulted in the memristor behavior transitioning from STP to LTP. The realization of those important synaptic functions in the Ti/HfO₂/TiO_x/Pt memristor may be suitable for applications in artificial neural systems.

Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits

The progress in the field of neural computation hinges on the use of hardware more efficient than the conventional microprocessors. Recent works have shown that mixed-signal integrated memristive circuits, especially their passive (0T1R) variety, may increase the neuromorphic network performance dramatically, leaving far behind their digital counterparts. The major obstacle, however, is immature memristor technology so that only limited functionality has been reported. Here we demonstrate operation of one-hidden layer perceptron classifier entirely in the mixed-signal integrated hardware, comprised of two passive 20 × 20 metal-oxide memristive crossbar arrays, board-integrated with discrete conventional components. The demonstrated network, whose hardware complexity is almost 10× higher as compared to previously reported functional classifier circuits based on passive memristive crossbars, achieves classification fidelity within 3% of that obtained in simulations, when using ex-situ training. The successful demonstration was facilitated by improvements in fabrication technology of memristors, specifically by lowering variations in their I-V characteristics.

Chitosan-Based Polysaccharide-Gated Flexible Indium Tin Oxide Synaptic Transistor with Learning Abilities

Recently, environment-friendly electronic devices are attracting increasing interest. "Green" artificial synapses with learning abilities are also interesting for neuromorphic platforms. Here, solution-processed chitosan-based polysaccharide electrolyte-gated indium tin oxide (ITO) synaptic transistors are fabricated on polyethylene terephthalate substrate. Good transistor performances against mechanical stress are observed. Short-term synaptic plasticities are mimicked on the proposed ITO synaptic transistor. When applying presynaptic and postsynaptic spikes on gate electrode and drain electrode respectively, spike-timing-dependent plasticity function is mimicked on the synaptic transistor. Transitions from sensory memory to short-term memory (STM) and from STM to long-term memory are also mimicked, demonstrating a "multistore model" brain memory. Furthermore, the flexible ITO synaptic transistor can be dissolved in deionized water easily, indicating potential green neuromorphic platform applications.

Short-Term Plasticity and Long-Term Potentiation in Artificial Biosynapses with Diffusive Dynamics

The development of electronic devices possessing the functionality of biological synapses is a crucial step toward replicating the capabilities of the human brain. Of the various materials that have been used to realize artificial synapses, renewable natural materials have the advantages of being abundant, inexpensive, biodegradable, and ecologically benign. In this study, we report a biocompatible artificial synapse based on a matrix of the biopolymer ι-carrageenan (ι-car), which exploits Ag dynamics. This artificial synapse emulates the short-term plasticity (STP), paired-pulse facilitation (PPF), and transition from STP to long-term potentiation (LTP) of a biological synapse. The above-mentioned characteristics are realized by exploiting the similarities between the Ag dynamics in the ι-car matrix and the Ca²⁺ dynamics in a biological synapse. By demonstrating a method that uses biomaterials and Ag dynamics to emulate synaptic functions, this study confirms that ι-car has the potential for constructing neuromorphic systems that use biocompatible artificial synapses.

Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network

Neuromorphic systems (hardware neural networks) derive inspiration from biological neural systems and are expected to be a computing breakthrough beyond conventional von Neumann architecture. Interestingly, in neuromorphic systems, the processing and storing of information can be performed simultaneously by modulating the connection strength of a synaptic device (i.e., synaptic weight). Previously investigated synaptic devices can emulate the functionality of biological synapses successfully by utilizing various nano-electronic phenomena; however, the impact of intrinsic synaptic device variability on the system performance has not yet been studied. Here, we perform a device-to-system level simulation of different synaptic device variation parameters in a designed neuromorphic system that has the potential for unsupervised learning and pattern recognition. The effects of variations in parameters such as the weight modulation nonlinearity (NL), the minimum-maximum weight (G_min and G_max), and the weight update margin (ΔG) on the pattern recognition accuracy are analyzed quantitatively. These simulation results can provide guidelines for the continued design and optimization of a synaptic device for realizing a functional large-scale neuromorphic computing system.

Synaptic Plasticity and Learning Behaviors Mimicked in Single Inorganic Synapses of Pt/HfOx/ZnOx/TiN Memristive System

In this work, a kind of new memristor with the simple structure of Pt/HfO_x/ZnO_x/TiN was fabricated completely via combination of thermal-atomic layer deposition (TALD) and plasma-enhanced ALD (PEALD). The synaptic plasticity and learning behaviors of Pt/HfO_x/ZnO_x/TiN memristive system have been investigated deeply. Multilevel resistance states are obtained by varying the programming voltage amplitudes during the pulse cycling. The device conductance can be continuously increased or decreased from cycle to cycle with better endurance characteristics up to about 3 × 10³ cycles. Several essential synaptic functions are simultaneously achieved in such a single double-layer of HfO_x/ZnO_x device, including nonlinear transmission properties, such as long-term plasticity (LTP), short-term plasticity (STP), and spike-timing-dependent plasticity. The transformation from STP to LTP induced by repetitive pulse stimulation is confirmed in Pt/HfO_x/ZnO_x/TiN memristive device. Above all, simple structure of Pt/HfO_x/ZnO_x/TiN by ALD technique is a kind of promising memristor device for applications in artificial neural network.

The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition

A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

Structurally Engineered Nanoporous Ta2O5-x Selector-Less Memristor for High Uniformity and Low Power Consumption

A memristor architecture based on metal-oxide materials would have great promise in achieving exceptional energy efficiency and higher scalability in next-generation electronic memory systems. Here, we propose a facile method for fabricating selector-less memristor arrays using an engineered nanoporous Ta₂O_5-x architecture. The device was fabricated in the form of crossbar arrays, and it functions as a switchable rectifier with a self-embedded nonlinear switching behavior and ultralow power consumption (∼2.7 × 10^-6 W), which results in effective suppression of crosstalk interference. In addition, we determined that the essential switching elements, such as the programming power, the sneak current, the nonlinearity value, and the device-to-device uniformity, could be enhanced by in-depth structural engineering of the pores in the Ta₂O_5-x layer. Our results, on the basis of the structural engineering of metal-oxide materials, could provide an attractive approach for fabricating simple and cost-efficient memristor arrays with acceptable device uniformity and low power consumption without the need for additional addressing selectors.

Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials

Emulation of biological synapses that perform memory and learning functions is an essential step toward realization of bioinspired neuromorphic systems. Artificial synaptic devices have been developed based mostly on inorganic materials and conventional semiconductor device fabrication processes. Here, we propose flexible biomemristor devices based on lignin by a simple solution process. Lignin is one of the most abundant organic polymers on Earth and is biocompatible, biodegradable, as well as environmentally benign. This memristor emulates several essential synaptic behaviors, including analog memory switching, short-term plasticity, long-term plasticity, spike-rate-dependent plasticity, and short-term to long-term transition. A flexible lignin-based artificial synapse device can be operated without noticeable degradation under mechanical bending test. These results suggest lignin can be a promising key component for artificial synapses and flexible electronic devices.

Scalability of voltage-controlled filamentary and nanometallic resistance memory devices

Much effort has been devoted to device and materials engineering to realize nanoscale resistance random access memory (RRAM) for practical applications, but a rational physical basis to be relied on to design scalable devices spanning many length scales is still lacking. In particular, there is no clear criterion for switching control in those RRAM devices in which resistance changes are limited to localized nanoscale filaments that experience concentrated heat, electric current and field. Here, we demonstrate voltage-controlled resistance switching, always at a constant characteristic critical voltage, for macro and nanodevices in both filamentary RRAM and nanometallic RRAM, and the latter switches uniformly and does not require a forming process. As a result, area-scalability can be achieved under a device-area-proportional current compliance for the low resistance state of the filamentary RRAM, and for both the low and high resistance states of the nanometallic RRAM. This finding will help design area-scalable RRAM at the nanoscale. It also establishes an analogy between RRAM and synapses, in which signal transmission is also voltage-controlled.

Ultrasensitive Memristive Synapses Based on Lightly Oxidized Sulfide Films

For biological synapses, high sensitivity is crucial for transmitting information quickly and accurately. Compared to biological synapses, memristive ones show a much lower sensitivity to electrical stimuli since much higher voltages are needed to induce synaptic plasticity. Yet, little attention has been paid to enhancing the sensitivity of synaptic devices. Here, electrochemical metallization memory cells based on lightly oxidized ZnS films are found to show highly controllable memristive switching with an ultralow SET voltage of several millivolts, which likely originates from a two-layer structure of ZnS films, i.e., the lightly oxidized and unoxidized layers, where the filament rupture/rejuvenation is confined to the two-layer interface region several nanometers in thickness due to different ion transport rates in these two layers. Based on such devices, an ultrasensitive memristive synapse is realized where the synaptic functions of both short-term plasticity and long-term potentiation are emulated by applying electrical stimuli several millivolts in amplitude, whose sensitivity greatly surpasses that of biological synapses. The dynamic processes of memorizing and forgetting are mimicked through a 5 × 5 memristive synapse array. In addition, the ultralow operating voltage provides another effective solution to the relatively high energy consumption of synaptic devices besides reducing the operating current and pulse width.

Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

Memristive devices, having a huge potential as artificial synapses for low-power neural networks, have received tremendous attention recently. Despite great achievements in demonstration of plasticity and learning functions, little progress has been made in the repeatable analog resistance states of memristive devices, which is, however, crucial for achieving controllable synaptic behavior. The controllable behavior of synapse is highly desired in building neural networks as it helps reduce training epochs and diminish error probability. Fundamentally, the poor repeatability of analog resistance states is closely associated with the random formation of conductive filaments, which consists of oxygen vacancies. In this work, graphene quantum dots (GQDs) are introduced into memristive devices. By virtue of the abundant oxygen anions released from GQDs, the GQDs can serve as nano oxygen-reservoirs and enhance the localization of filament formation. As a result, analog resistance states with highly tight distribution are achieved with nearly 85% reduction in variations. In addition the insertion of GQDs can alter the energy band alignment and boost the tunneling current, which leads to significant reduction in both switching voltages and their distribution variations. This work may pave the way for achieving artificial neural networks with accurate and efficient learning capability.

Tuning analog resistive switching and plasticity in bilayer transition metal oxide based memristive synapses

The existence of rich suboxide phases is favorable for increasing the number of weight states in transition metal oxide synapses.

3D Ta/TaO x /TiO2/Ti synaptic array and linearity tuning of weight update for hardware neural network applications

The implementation of highly anticipated hardware neural networks (HNNs) hinges largely on the successful development of a low-power, high-density, and reliable analog electronic synaptic array. In this study, we demonstrate a two-layer Ta/TaO x /TiO2/Ti cross-point synaptic array that emulates the high-density three-dimensional network architecture of human brains. Excellent uniformity and reproducibility among intralayer and interlayer cells were realized. Moreover, at least 50 analog synaptic weight states could be precisely controlled with minimal drifting during a cycling endurance test of 5000 training pulses at an operating voltage of 3 V. We also propose a new state-independent bipolar-pulse-training scheme to improve the linearity of weight updates. The improved linearity considerably enhances the fault tolerance of HNNs, thus improving the training accuracy.