Learning abilities achieved by a single solid-state atomic switch

Toward a Brain-Neuromorphics Interface

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

Building a depletion-region width modulation model and realizing memory characteristics in PN heterostructure devices

Memristive systems have potential applications in nonvolatile memories and even unexplored functionalities in electronics. However, progress has been delayed by difficulties in the controllability of memory behaviors and the dependence on material conductivity. Considering this, a new depletion-region width modulation model is proposed to realize and explain memory characteristics. The coexistence of memristive and memcapacitive behaviors is demonstrated in p-CuAlO2/n-ZnO, p+-Si/n-ZnO and p-NiO/n-ZnO heterostructure devices. A high external electric field induces the migration of oxygen ions and electrons/holes between the p-type and n-type semiconductor layers. It can regulate the oxygen vacancy concentration of the n-type side and cation vacancy concentration of the p-type side, changing the depletion-region width and modulating device conductivity and capacitance. Several essential synaptic functions were accurately imitated, including spike-timing-dependent plasticity (STDP) and "learning-experience" behaviors. This work provides new opportunities in fabricating a memristor and memcapacitor based on a PN heterostructure for synaptic simulation.

Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions

Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.

Toward Practical Single-Molecule/Atom Switches

Electronic switches have been considered to be one of the most important components of contemporary electronic circuits for processing and storing digital information. Fabricating functional devices with building blocks of atomic/molecular switches can greatly promote the minimization of the devices and meet the requirement of high integration. This review highlights key developments in the fabrication and application of molecular switching devices. This overview offers valuable insights into the switching mechanisms under various stimuli, emphasizing structural and energy state changes in the core molecules. Beyond the molecular switches, typical individual metal atomic switches are further introduced. A critical discussion of the main challenges for realizing and developing practical molecular/atomic switches is provided. These analyses and summaries will contribute to a comprehensive understanding of the switch mechanisms, providing guidance for the rational design of functional nanoswitch devices toward practical applications.

Liquid-Liquid Interface-Assisted Self-Assembly of Ag-Doped ZnO Nanosheets for Atomic Switch Application

Developing facile and inexpensive methods for obtaining large-area two-dimensional semiconducting nanosheets is highly desirable for mass-scale device application. Here, we report a method for producing uniform and large-area films of a Ag-doped ZnO (AZO) nanosheet network via self-assembly at the hexane-water interface by controlling the solute/solvent ratio. The self-assembled film comprises of uniformly tiled nanosheets with size ∼1 μm and thicknesses∼60-100 nm. Using these films in a Pt/AZO/Ag structure, an atomic switch operation is realized. The switching mechanism is found to be governed by electrochemical metallization with nucleation as the rate-limiting step. Our results establish the protocol for large-scale device applications of AZO nanosheets for exploring advanced atomic switch-based neuromorphic systems.

Realization of Artificial Nerve Synapses Based on Biological Threshold Resistive Random Access Memory

A one-selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high-density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10-7  A), a high ON/OFF current ratio (106 ), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA-based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein-based 1S1R array to achieve high-density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.).

Pseudo-transistors for emerging neuromorphic electronics

Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.

Metal doped polyaniline as neuromorphic circuit elements for in-materia computing

Polyaniline-based atomic switches are material building blocks whose nanoscale structure and resultant neuromorphic character provide a new physical substrate for the development next-generation, nanoarchitectonic-enabled computing systems. Metal ion-doped devices consisting of a Ag/metal ion doped polyaniline/Pt sandwich structure were fabricated using an in situ wet process. The devices exhibited repeatable resistive switching between high (ON) and low (OFF) conductance states in both Ag⁺ and Cu²⁺ ion-doped devices. The threshold voltage for switching was>0.8 V and average ON/OFF conductance ratios (30 cycles for 3 samples) were 13 and 16 for Ag⁺ and Cu²⁺ devices, respectively. The ON state duration was determined by the decay to an OFF state after pulsed voltages of differing amplitude and frequency. The switching behaviour is analagous to short-term (STM) and long-term (LTM) memories of biological synapses. Memristive behaviour and evidence of quantized conductance were also observed and interpreted in terms of metal filament formation bridging the metal doped polymer layer. The successful realization of these properties within physical material systems indicate polyaniline frameworks as suitable neuromorphic substrates for in materia computing.

Recent advances in neuromorphic transistors for artificial perception applications: FOCUS ISSUE REVIEW

Conventional von Neumann architecture is insufficient in establishing artificial intelligence (AI) in terms of energy efficiency, computing in memory and dynamic learning. Delightedly, rapid developments in neuromorphic computing provide a new paradigm to solve this dilemma. Furthermore, neuromorphic devices that can realize synaptic plasticity and neuromorphic function have extraordinary significance for neuromorphic system. A three-terminal neuromorphic transistor is one of the typical representatives. In addition, human body has five senses, including vision, touch, auditory sense, olfactory sense and gustatory sense, providing abundant information for brain. Inspired by the human perception system, developments in artificial perception system will give new vitality to intelligent robots. This review discusses the operation mechanism, function and application of neuromorphic transistors. The latest progresses in artificial perception systems based on neuromorphic transistors are provided. Finally, the opportunities and challenges of artificial perception systems are summarized.

High Linearity Synaptic Devices Using Ar Plasma Treatment on HfO2 Thin Film with Non-Identical Pulse Waveforms

We enhanced the device uniformity for reliable memory performances by increasing the device surface roughness by exposing the HfO₂ thin film surface to argon (Ar) plasma. The results showed significant improvements in electrical and synaptic properties, including memory window, linearity, pattern recognition accuracy, and synaptic weight modulations. Furthermore, we proposed a non-identical pulse waveform for further improvement in linearity accuracy. From the simulation results, the Ar plasma processing device using the designed waveform as the input signals significantly improved the off-chip training and inference accuracy, achieving 96.3% training accuracy and 97.1% inference accuracy in only 10 training cycles.

Surface acoustic wave controlled skyrmion-based synapse devices

Magnetic skyrmions, which are particle-like spin structures, are promising information carriers for neuromorphic computing devices due to their topological stability and nanoscale size. In this work, we propose controlling magnetic skyrmions by electric-field-excited surface acoustic waves in neuromorphic computing device structures. Our micromagnetic simulations show that the number of created skyrmions, which emulates the synaptic weight parameter, increases monotonically with increases in the amplitude of the surface acoustic waves. Additionally, the efficiency of skyrmion creation is investigated systemically with a wide range of magnetic parameters, and the optimal values are presented accordingly. Finally, the functionalities of short-term plasticity and long-term potentiation are demonstrated via skyrmion excitation by a sequence of surface acoustic waves with different intervals. The application of surface acoustic waves in skyrmionic neuromorphic computing devices paves a novel approach to low-power computing systems.

Neuromorphic Devices for Bionic Sensing and Perception

Neuromorphic devices that can emulate the bionic sensory and perceptual functions of neural systems have great applications in personal healthcare monitoring, neuro-prosthetics, and human-machine interfaces. In order to realize bionic sensing and perception, it's crucial to prepare neuromorphic devices with the function of perceiving environment in real-time. Up to now, lots of efforts have been made in the incorporation of the bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. In this review, we first introduce neuromorphic devices based on diverse materials and mechanisms. Then we summarize the progress made in the emulation of biological sensing and perception systems. Finally, the challenges and opportunities in these fields are also discussed.

Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks

Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual's adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

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.

Magnetic skyrmions for unconventional computing

Improvements in computing performance have significantly slowed down over the past few years owing to the intrinsic limitations of computing hardware. However, the demand for data computing has increased exponentially. To solve this problem, tremendous attention has been focused on the continuous scaling of Moore's law as well as the advanced non-von Neumann computing architecture. A rich variety of unconventional computing paradigms has been devised with the rapid development of nanoscale devices. Magnetic skyrmions, spin swirling quasiparticles, have been endowed with great expectations for unconventional computing due to their potential as the smallest information carriers by exploiting their physics and dynamics. In this paper, we provide an overview of the recent progress of skyrmion-based unconventional computing from a joint device-application perspective. This paper aims to build up a panoramic picture, analyze the remaining challenges, and most importantly to shed light on the outlook of skyrmion based unconventional computing for interdisciplinary researchers.

Modulation of the plasticity of an all-metal oxide synaptic transistor via laser irradiation

Artificial intelligence devices that can mimic human brains are the foundation for building future artificial neural networks. A key step in mimicking biological neural systems is the modulation of synaptic weight, which is mainly achieved by various engineering approaches using material design, or modification of the device structure. Here, we realize the modulation of the synaptic weight of a Ta₂O₅/ITO-based all-metal oxide synaptic transistor via laser irradiation. Prior to the deposition of the active layer and electrodes, a femtosecond laser was used to irradiate the surface of the insulator layer. Typical synaptic characteristics such as excitatory postsynaptic current, paired pulse facilitation and long-term potentiation were successfully simulated under different laser intensities and scanning rates. In particular, we demonstrate for the first time that laser irradiation could control the quantity of oxygen vacancies in the Ta₂O₅ thin film, leading to precise modulation of the synaptic weight. Our research provides an instantaneous (<1 s), convenient and low-temperature approach to improving synaptic behaviors, which could be promising for neuromorphic computing hardware design.

Artificial Sensory Memory

Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intelligence, which would profoundly advance a broad spectrum of applications, such as prosthetics, robotics, and cyborg systems. Here, the recent developments in the design and fabrication of artificial sensory memory devices are summarized and their applications in recognition, manipulation, and learning are highlighted. The emergence of such devices benefits from recent progress in both bioinspired sensing and neuromorphic engineering technologies and derives from abundant inspiration and benchmarks from an improved understanding of biological sensory processing. Increasing attention to this area would offer unprecedented opportunities toward new hardware architecture of artificial intelligence, which could extend the capabilities of digital systems with emotional/psychological attributes. Pending challenges are also addressed to aspects such as integration level, energy efficiency, and functionality, which would undoubtedly shed light on the future development of translational implementations.

A Novel Characterization and Performance Measurement of Memristor Devices for Synaptic Emulators in Advanced Neuro-Computing

The advanced neuro-computing field requires new memristor devices with great potential as synaptic emulators between pre- and postsynaptic neurons. This paper presents memristor devices with TiO2 Nanoparticles (NPs)/Ag(Silver) and Titanium Dioxide (TiO2) Nanoparticles (NPs)/Au(Gold) electrodes for synaptic emulators in an advanced neurocomputing application. A comparative study between Ag(Silver)- and Au(Gold)-based memristor devices is presented where the Ag electrode provides the improved performance, as compared to the Au electrode. Device characterization is observed by the Scanning Electron Microscope (SEM) image, which displays the grown electrode, while the morphology of nanoparticles (NPs) is verified by Atomic Force Microscopy (AFM). The resistive switching (RS) phenomena observed in Ag/TiO2 and Au/TiO2 shows the sweeping mechanism for low resistance and high resistance states. The resistive switching time of Au/TiO2 NPs and Ag/TiO2 NPs is calculated, while the theoretical validation of the memory window demonstrates memristor behavior as a synaptic emulator. Measurement of the capacitor-voltage curve shows that the memristor with Ag contact is a good candidate for charge storage as compared to Au. The classification of 3 × 3 pixel black/white image is demonstrated by the 3 × 3 cross bar memristor with pre- and post-neuron system. The proposed memristor devices with the Ag electrode demonstrate the adequate performance compared to the Au electrode, and may present noteworthy advantages in the field of neuromorphic computing.

Decoding the metallic bridging dynamics in nanogap atomic switches

Atomic switches are promising candidates as the basic building blocks for large-scale neuromorphic networks due to their tunable switching behaviors. Several neuromorphic components based on atomic switches have been demonstrated, including artificial synapses, artificial neurons and short-term to long-term memory, making it possible to construct neuromorphic systems using a unified device. Although the mechanism of atomic switches has been actively studied, most of the discussions in previous studies are qualitative and failed to provide a comprehensive view of the dynamics that can precisely describe the metallic bridging under an electric field. In this paper, we designed a gap-type atomic switch and realized various switching behaviors, including both volatile and non-volatile resistive switching. Employing advanced microanalysis technology, we experimentally studied the switching mechanism and captured the nanoscale metallic filament in gap-type atomic switches. Furthermore, based on the experimental findings as well as on the electrochemistry fundamental and electron tunneling effect, we proposed a physical model that precisely reproduced the sophisticated switching behaviors. Our model mathematically described the growth/shrinkage dynamics of nanoscale metallic filaments, providing a direction for studying the switching behaviors from a quantitative view. The simulation results are in good agreement with the experimental findings in both DC sweep and pulse operation modes. In addition, we have demonstrated neuronal tonic spiking and short-term to long-term memory in experiment and simulation, indicating that our model can be applied to the circuit level simulation of large-scale atomic switch arrays for neuromorphic applications.

Emergent dynamics of neuromorphic nanowire networks

Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.

Tailored nanoplateau and nanochannel structures using solution-processed rutile TiO2 thin films for complementary and bipolar switching characteristics

We synthesized two different nanostructures of rutile TiO₂ (r-TiO₂) thin films on a fluorine-doped tin oxide (FTO) substrate at the lowest temperature reported until now and fabricated resistive random access memory (RRAM) devices with these r-TiO₂ thin films having the stacking sequence of Ag/r-TiO₂/FTO. Complementary resistive switching (CRS) and bipolar resistive switching (BRS) were observed in different thicknesses of r-TiO₂ based devices. Benefiting from the in situ growth of the solution processed thin films and modulating the reaction growth rates, we successfully attained two different morphologies of r-TiO₂ with a nanoplateau at a controlled deposition rate and pre-defined nanochannels at a higher deposition rate. The RRAM devices with nano-plateaus of r-TiO₂ showed excellent CRS as well as unprecedented simultaneous observations of BRS. These CRS and BRS characteristics were reversible and reproducible. On the other hand, the tailored pre-defined nanochannels in r-TiO₂ led to forming-free BRS with a pulse endurance higher than 10⁷ without any degradation in the high and low resistance states. We propose a plausible switching mechanism of these unprecedented events using various physical and electrical characterization studies of low-temperature processed r-TiO₂ RRAM devices. This work suggests the importance of solution-processed thin film engineering for RRAM switching with reliable and reproducible characteristics.

Recent Progress in Three-Terminal Artificial Synapses: From Device to System

Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows changes in synaptic strength that make a brain capable of learning from experience. During development of neuromorphic electronics, great efforts have been made to design and fabricate electronic devices that emulate synapses. Three-terminal artificial synapses have the merits of concurrently transmitting signals and learning. Inorganic and organic electronic synapses have mimicked plasticity and learning. Optoelectronic synapses and photonic synapses have the prospective benefits of low electrical energy loss, high bandwidth, and mechanical robustness. These artificial synapses provide new opportunities for the development of neuromorphic systems that can use parallel processing to manipulate datasets in real time. Synaptic devices have also been used to build artificial sensory systems. Here, recent progress in the development and application of three-terminal artificial synapses and artificial sensory systems is reviewed.

Tunable Resistive Switching Enabled by Malleable Redox Reaction in the Nano-Vacuum Gap

Neuromorphic computing has emerged as a highly promising alternative to conventional computing. The key to constructing a large-scale neural network in hardware for neuromorphic computing is to develop artificial neurons with leaky integrate-and-fire behavior and artificial synapses with synaptic plasticity using nanodevices. So far, these two basic computing elements have been built in separate devices using different materials and technologies, which poses a significant challenge to system design and manufacturing. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap between a bottom electrode and a mixed-ionic-electronic-conductor (MIEC) layer. Through redox reaction on the MIEC surface, metallic filaments dynamically grew within the nano-vacuum gap. The nano-vacuum gap provided an additional control factor for controlling the evolution dynamics of metallic filaments by tuning the electron tunneling efficiency, in analogy to a pseudo-three-terminal device, resulting in tunable switching behavior in various forms from volatile to nonvolatile switching in a single device. Our device demonstrated cross-functions, in particular, tunable neuronal firing and synaptic plasticity on demand, providing seamless integration for building large-scale artificial neural networks for neuromorphic computing.

Stretchable and conformable synapse memristors for wearable and implantable electronics

Stretchable and conformable synapse memristors that can emulate the behaviour of the biological neural system and well adhere onto the curved surfaces simultaneously are desirable for the development of imperceptible wearable and implantable neuromorphic computing systems. Previous synapse memristors have been mainly limited to rigid substrates. Herein, a stretchable and conformable memristor with fundamental synaptic functions including potentiation/depression characteristics, long/short-term plasticity (STP and LTP), "learning-forgetting-relearning" behaviour, and spike-rate-dependent and spike-amplitude-dependent plasticity is demonstrated based on highly elastic Ag nanoparticle-doped thermoplastic polyurethanes (TPU : Ag NPs) and polydimethylsiloxane (PDMS). The memristor can be well operated even at 60% strain and can be well conformed onto the curved surfaces. The formed conductive filament (CF) obtained from the movement of Ag nanoparticle clusters under the locally enhanced electric field gives rise to resistance switching of our memristor. These results indicate a feasible strategy to realize stretchable and conformable synaptic devices for the development of new-generation artificial intelligence computers.

Mediating Short-Term Plasticity in an Artificial Memristive Synapse by the Orientation of Silica Mesopores

Memristive synapses based on resistive switching are promising electronic devices that emulate the synaptic plasticity in neural systems. Short-term plasticity (STP), reflecting a temporal strengthening of the synaptic connection, allows artificial synapses to perform critical computational functions, such as fast response and information filtering. To mediate this fundamental property in memristive electronic devices, the regulation of the dynamic resistive change is necessary for an artificial synapse. Here, it is demonstrated that the orientation of mesopores in the dielectric silica layer can be used to modulate the STP of an artificial memristive synapse. The dielectric silica layer with vertical mesopores can facilitate the formation of a conductive pathway, which underlies a lower set voltage (≈1.0 V) compared to these with parallel mesopores (≈1.2 V) and dense amorphous silica (≈2.0 V). Also, the artificial memristive synapses with vertical mesopores exhibit the fastest current increase by successive voltage pulses. Finally, oriented silica mesopores are designed for varying the relaxation time of memory, and thus the successful mediation of STP is achieved. The implementation of mesoporous orientation provides a new perspective for engineering artificial synapses with multilevel learning and forgetting capability, which is essential for neuromorphic computing.

Oxygen vacancy migration/diffusion induced synaptic plasticity in a single titanate nanobelt

Neuromorphic computational systems that emulate biological synapses in the human brain are fundamental in the development of artificial intelligence protocols beyond the standard von Neumann architecture. Such systems require new types of building blocks, such as memristors that access a quasi-continuous and wide range of conductive states, which is still an obstacle for the realization of high-efficiency and large-capacity learning in neuromorphoric simulation. Here, we introduce hydrogen and sodium titanate nanobelts, the intermediate products of hydrothermal synthesis of TiO2 nanobelts, to emulate the synaptic behavior. Devices incorporating a single titanate nanobelt demonstrate robust and reliable synaptic functions, including excitatory postsynaptic current, paired pulse facilitation, short term plasticity, potentiation and depression, as well as learning-forgetting behavior. In particular, the gradual modulation of conductive states in the single nanobelt device can be achieved by a large number of identical pulses. The mechanism for synaptic functionality of the titanate nanobelt device is attributed to the competition between an electric field driven migration of oxygen vacancies and a thermally induced spontaneous diffusion. These results provide insight into the potential use of titanate nanobelts in synaptic applications requiring continuously addressable states coupled with high processing efficiency.

Microbial nanowires - Electron transport and the role of synthetic analogues

Electron transfer is central to cellular life, from photosynthesis to respiration. In the case of anaerobic respiration, some microbes have extracellular appendages that can be utilised to transport electrons over great distances. Two model organisms heavily studied in this arena are Shewanella oneidensis and Geobacter sulfurreducens. There is some debate over how, in particular, the Geobacter sulfurreducens nanowires (formed from pilin nanofilaments) are capable of achieving the impressive feats of natural conductivity that they display. In this article, we outline the mechanisms of electron transfer through delocalised electron transport, quantum tunnelling, and hopping as they pertain to biomaterials. These are described along with existing examples of the different types of conductivity observed in natural systems such as DNA and proteins in order to provide context for understanding the complexities involved in studying the electron transport properties of these unique nanowires. We then introduce some synthetic analogues, made using peptides, which may assist in resolving this debate. Microbial nanowires and the synthetic analogues thereof are of particular interest, not just for biogeochemistry, but also for the exciting potential bioelectronic and clinical applications as covered in the final section of the review.

STATEMENT OF SIGNIFICANCE

Some microbes have extracellular appendages that transport electrons over vast distances in order to respire, such as the dissimilatory metal-reducing bacteria Geobacter sulfurreducens. There is significant debate over how G. sulfurreducens nanowires are capable of achieving the impressive feats of natural conductivity that they display: This mechanism is a fundamental scientific challenge, with important environmental and technological implications. Through outlining the techniques and outcomes of investigations into the mechanisms of such protein-based nanofibrils, we provide a platform for the general study of the electronic properties of biomaterials. The implications are broad-reaching, with fundamental investigations into electron transfer processes in natural and biomimetic materials underway. From these studies, applications in the medical, energy, and IT industries can be developed utilising bioelectronics.

Realization of synaptic learning and memory functions in Y2O3 based memristive device fabricated by dual ion beam sputtering

Single synaptic device with inherent learning and memory functions is demonstrated based on a forming-free amorphous Y₂O₃ (yttria) memristor fabricated by dual ion beam sputtering system. Synaptic functions such as nonlinear transmission characteristics, long-term plasticity, short-term plasticity and 'learning behavior (LB)' are achieved using a single synaptic device based on cost-effective metal-insulator-semiconductor (MIS) structure. An 'LB' function is demonstrated, for the first time in the literature, for a yttria based memristor, which bears a resemblance to certain memory functions of biological systems. The realization of key synaptic functions in a cost-effective MIS structure would promote much cheaper synapse for artificial neural network.

Mimicking the brain functions of learning, forgetting and explicit/implicit memories with SrTiO3-based memristive devices

To implement the complex brain functions of learning, forgetting and memory in a single electronic device is very advantageous for realizing artificial intelligence. As a proof of concept, memristive devices with a simple structure of Ni/Nb-SrTiO₃/Ti were investigated in this work. The functions of learning, forgetting and memory were successfully mimicked using the memristive devices, and the "time-saving" effect of implicit memory was also demonstrated. The physics behind the brain functions is simply the modulation of the Schottky barrier at the Ni/SrTiO₃ interface. The realization of various psychological functions in a single device simplifies the construction of the artificial neural network and facilitates the advent of artificial intelligence.

Compliance-Free, Digital SET and Analog RESET Synaptic Characteristics of Sub-Tantalum Oxide Based Neuromorphic Device

A two terminal semiconducting device like a memristor is indispensable to emulate the function of synapse in the working memory. The analog switching characteristics of memristor play a vital role in the emulation of biological synapses. The application of consecutive voltage sweeps or pulses (action potentials) changes the conductivity of the memristor which is considered as the fundamental cause of the synaptic plasticity. In this study, a neuromorphic device using an in-situ growth of sub-tantalum oxide switching layer is fabricated, which exhibits the digital SET and analog RESET switching with an electroforming process without any compliance current (compliance free). The process of electroforming and SET is observed at the positive sweeps of +2.4 V and +0.86 V, respectively, while multilevel RESET is observed with the consecutive negative sweeps in the range of 0 V to -1.2 V. The movement of oxygen vacancies and gradual change in the anatomy of the filament is attributed to digital SET and analog RESET switching characteristics. For the Ti/Ta₂O_3-x/Pt neuromorphic device, the Ti top and Pt bottom electrodes are considered as counterparts of the pre-synaptic input terminal and a post-synaptic output terminal, respectively.

Ultralow power artificial synapses using nanotextured magnetic Josephson junctions

Neuromorphic computing promises to markedly improve the efficiency of certain computational tasks, such as perception and decision-making. Although software and specialized hardware implementations of neural networks have made tremendous accomplishments, both implementations are still many orders of magnitude less energy efficient than the human brain. We demonstrate a new form of artificial synapse based on dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier. The spiking energy per pulse varies with the magnetic configuration, but in our demonstration devices, the spiking energy is always less than 1 aJ. This compares very favorably with the roughly 10 fJ per synaptic event in the human brain. Each artificial synapse is composed of a Si barrier containing Mn nanoclusters with superconducting Nb electrodes. The critical current of each synapse junction, which is analogous to the synaptic weight, can be tuned using input voltage spikes that change the spin alignment of Mn nanoclusters. We demonstrate synaptic weight training with electrical pulses as small as 3 aJ. Further, the Josephson plasma frequencies of the devices, which determine the dynamical time scales, all exceed 100 GHz. These new artificial synapses provide a significant step toward a neuromorphic platform that is faster, more energy-efficient, and thus can attain far greater complexity than has been demonstrated with other technologies.

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.

Recent Advances on Neuromorphic Systems Using Phase-Change Materials

Realization of brain-like computer has always been human's ultimate dream. Today, the possibility of having this dream come true has been significantly boosted due to the advent of several emerging non-volatile memory devices. Within these innovative technologies, phase-change memory device has been commonly regarded as the most promising candidate to imitate the biological brain, owing to its excellent scalability, fast switching speed, and low energy consumption. In this context, a detailed review concerning the physical principles of the neuromorphic circuit using phase-change materials as well as a comprehensive introduction of the currently available phase-change neuromorphic prototypes becomes imperative for scientists to continuously progress the technology of artificial neural networks. In this paper, we first present the biological mechanism of human brain, followed by a brief discussion about physical properties of phase-change materials that recently receive a widespread application on non-volatile memory field. We then survey recent research on different types of neuromorphic circuits using phase-change materials in terms of their respective geometrical architecture and physical schemes to reproduce the biological events of human brain, in particular for spike-time-dependent plasticity. The relevant virtues and limitations of these devices are also evaluated. Finally, the future prospect of the neuromorphic circuit based on phase-change technologies is envisioned.

Electronic imitation of behavioral and psychological synaptic activities using TiOx/Al2O3-based memristor devices

Seeking an effective electronic synapse to emulate biological synaptic behavior is fundamental for building brain-inspired computers. An emerging two-terminal memristor, in which the conductance can be gradually modulated by external electrical stimuli, is widely considered as the strongest competitor of the electronic synapse. Here, we show the capability of TiO_x/Al₂O₃-based memristor devices to imitate synaptic behaviors. Along with analog resistive switching performances, the devices replicate the bio-synapse behaviors of potentiation/depression, short-term-plasticity, and long-term-potentiation, which show that TiO_x/Al₂O₃-based memristors may be useful as electronic synapses. The essential memorizing capabilities of the brain are dependent on the connection strength between neurons, and the memory types change from short-term memory to long-term memory. In the TiO_x/Al₂O₃-based electronic synaptic junction, the memorizing levels can change their state via a standard rehearsal process and also via newly introduced process called "impact of event" i.e. the impact of pulse amplitude, and the width of the input pulse. The devices show a short-term to long-term memory effect with the introduction of intermediate mezzanine memory. The experimental achievements using the TiO_x/Al₂O₃ electronic synapses are finally psychologically modeled by considering the mezzanine level. It is highly recommended that similar phenomena should be investigated for other memristor systems to check the authenticity of this model.

Memristive stochastic plasticity enables mimicking of neural synchrony: Memristive circuit emulates an optical illusion

The human brain is able to integrate a myriad of information in an enormous and massively parallel network of neurons that are divided into functionally specialized regions such as the visual cortex, auditory cortex, or dorsolateral prefrontal cortex. Each of these regions participates as a context-dependent, self-organized, and transient subnetwork, which is shifted by changes in attention every 0.5 to 2 s. This leads to one of the most puzzling issues in cognitive neuroscience, well known as the "binding problem." The concept of neural synchronization tries to explain the problem by encoding information using coherent states, which temporally patterns neural activity. We show that memristive devices, that is, a two-terminal variable resistor that changes its resistance depending on the previous charge flow, allow a new degree of freedom for this concept: a local memory that supports transient connectivity patterns in oscillator networks. On the basis of the probability distribution of the resistance switching process of Ag-doped titanium dioxide memristive devices, a local plasticity model is proposed, which causes an autonomous phase and frequency locking in an oscillator network. To illustrate the performance of the proposed computing paradigm, the temporal binding problem is investigated in a network of memristively coupled self-sustained van der Pol oscillators. We show evidence that the implemented network allows achievement of the transition from asynchronous to multiple synchronous states, which opens a new pathway toward the construction of cognitive electronics.

Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability

If a three-dimensional physical electronic system emulating synapse networks could be built, that would be a significant step toward neuromorphic computing. However, the fabrication complexity of complementary metal-oxide-semiconductor architectures impedes the achievement of three-dimensional interconnectivity, high-device density, or flexibility. Here we report flexible three-dimensional artificial chemical synapse networks, in which two-terminal memristive devices, namely, electronic synapses (e-synapses), are connected by vertically stacking crossbar electrodes. The e-synapses resemble the key features of biological synapses: unilateral connection, long-term potentiation/depression, a spike-timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow-power consumption. The three-dimensional artificial synapse networks enable a direct emulation of correlated learning and trainable memory capability with strong tolerances to input faults and variations, which shows the feasibility of using them in futuristic electronic devices and can provide a physical platform for the realization of smart memories and machine learning and for operation of the complex algorithms involving hierarchical neural networks.High-density information storage calls for the development of modern electronics with multiple stacking architectures that increase the complexity of three-dimensional interconnectivity. Here, Wu et al. build a stacked yet flexible artificial synapse network using layer-by-layer solution processing.

A Synaptic Transistor based on Quasi-2D Molybdenum Oxide

Biological synapses store and process information simultaneously by tuning the connection between two neighboring neurons. Such functionality inspires the task of hardware implementation of neuromorphic computing systems. Ionic/electronic hybrid three-terminal memristive devices, in which the channel conductance can be modulated according to the history of applied voltage and current, provide a more promising way of emulating synapses by a substantial reduction in complexity and energy consumption. 2D van der Waals materials with single or few layers of crystal unit cells have been a widespread innovation in three-terminal electronic devices. However, less attention has been paid to 2D transition-metal oxides, which have good stability and technique compatibility. Here, nanoscale three-terminal memristive transistors based on quasi-2D α-phase molybdenum oxide (α-MoO₃ ) to emulate biological synapses are presented. The essential synaptic behaviors, such as excitatory postsynaptic current, depression and potentiation of synaptic weight, and paired-pulse facilitation, as well as the transition of short-term plasticity to long-term potentiation, are demonstrated in the three-terminal devices. These results provide an insight into the potential application of 2D transition-metal oxides for synaptic devices with high scaling ability, low energy consumption, and high processing efficiency.

Time-decay Memristive Behavior and diffusive dynamics in one forget process operated by a 3D vertical Pt/Ta2O5-x/W device

A time-decay resistive switching memory using a 3D vertical Pt/Ta2O5-x/W device architecture is demonstrated, in which horizontal W electrodes were fabricated, and vertical Pt electrodes was formed at the sidewall after oxide was deposited. Unlike conventional resistive switching, which usually form a conductive filament connect two electrodes, a weak conductive filament was formed from bottom electrode W to near top electrode Pt. The memory can be recovered with a time scale when the electrical stimulation is removed. However, different decay behaviors were observed in one decay curve, including rapid decay and slow decay processes. This can be a good simulation of different stages of forgetting. By a combination of the current decay fitting and the conductive analysis, the rapid decay and slow decay processes correspond to ion diffusion and electron detrapping, respectively.

Synaptic Plasticity Selectively Activated by Polarization-Dependent Energy-Efficient Ion Migration in an Ultrathin Ferroelectric Tunnel Junction

Selectively activated inorganic synaptic devices, showing a high on/off ratio, ultrasmall dimensions, low power consumption, and short programming time, are required to emulate the functions of high-capacity and energy-efficient reconfigurable human neural systems combining information storage and processing ( Li et al. Sci. Rep. 2014 , 4 , 4096 ). Here, we demonstrate that such a synaptic device is realized using a Ag/PbZr_0.52Ti_0.48O₃ (PZT)/La_0.8Sr_0.2MnO₃ (LSMO) ferroelectric tunnel junction (FTJ) with ultrathin PZT (thickness of ∼4 nm). Ag ion migration through the very thin FTJ enables a large on/off ratio (10⁷) and low energy consumption (potentiation energy consumption = ∼22 aJ and depression energy consumption = ∼2.5 pJ). In addition, the simple alignment of the downward polarization in PZT selectively activates the synaptic plasticity of the FTJ and the transition from short-term plasticity to long-term potentiation.

Mimicking Classical Conditioning Based on a Single Flexible Memristor

The mimicking of classical conditioning, including acquisition, extinction, recovery, and generalization, can be efficiently achieved by using a single flexible memristor. In particular, the experiment of Pavlov's dog is successfully demonstrated. This demonstration paves the way for reproducing advanced neural processes and provides a frontier approach to the design of artificial-intelligence systems with dramatically reduced complexity.

Conducting-insulating transition in adiabatic memristive networks

The development of neuromorphic systems based on memristive elements-resistors with memory-requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes. Our work sheds considerable light on the statistical properties of memristive networks that are presently studied both for unconventional computing and as models of neural networks.

Chameleonic electrochemical metallization cells: dual-layer solid electrolyte-inducing various switching behaviours

We present 'unusual' resistive switching behaviours in electrochemical metallization (ECM) cells utilizing a dual-layer (SiOx/GeSex: SiOx on GeSex) solid electrolyte (SE). The observed switching behaviour markedly varies with the thickness of the upper SiOx layer and compliance current: (i) monostable switching, (ii) counter-eightwise bipolar switching, and (iii) combination of monostable and eightwise bipolar switching behaviours. Focusing on cases (i) and (iii), electrical and chemical analyses on these chameleonic cells were performed in an attempt to gain clues to the understanding of the observed complexity. The chemical analysis indicated the upper SiOx layer as a chemical potential well for Cu ions-Cu ions were largely confined in the well. This non-uniform distribution of Cu across the SE perhaps hints at the mechanism for the complex behaviour; it may be a 'zero-sum game' between SiOx and GeSex layers, in which the two layers fight over the limited number of Cu atoms/ions.

A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy

Memristors are non-volatile nano-resistors which resistance can be tuned by applied currents or voltages and set to a large number of levels. Thanks to these properties, memristors are ideal building blocks for a number of applications such as multilevel non-volatile memories and artificial nano-synapses, which are the focus of this work. A key point towards the development of large scale memristive neuromorphic hardware is to build these neural networks with a memristor technology compatible with the best candidates for the future mainstream non-volatile memories. Here we show the first experimental achievement of a multilevel memristor compatible with spin-torque magnetic random access memories. The resistive switching in our spin-torque memristor is linked to the displacement of a magnetic domain wall by spin-torques in a perpendicularly magnetized magnetic tunnel junction. We demonstrate that our magnetic synapse has a large number of intermediate resistance states, sufficient for neural computation. Moreover, we show that engineering the device geometry allows leveraging the most efficient spin torque to displace the magnetic domain wall at low current densities and thus to minimize the energy cost of our memristor. Our results pave the way for spin-torque based analog magnetic neural computation.

Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing

Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament growth (dissolution) dynamics with time, therefore significantly improving the conductance modulation linearity that is desirable in neuromorphic systems. In addition, the introduction of the DLL also served to reduce the power consumption of the memristor, and important synaptic learning rules in biological brains such as spike timing dependent plasticity were successfully implemented using these optimized devices. This study could provide general implications for continued optimizations of memristor performance for neuromorphic applications, by carefully tuning the dynamics involved in filament growth and dissolution.

Organometal Halide Perovskite Artificial Synapses

Organometal halide perovskite synaptic devices are fabricated; they emulate important working principles of a biological synapse, including excitatory postsynaptic current, paired-pulse facilitation, short-term plasticity, long-term plasticity, and spike-timing dependent plasticity. These properties originate from possible ion migration in the ion-rich perovskite matrix. This work has extensive applicability and practical significance in neuromorphic electronics.

Organic core-sheath nanowire artificial synapses with femtojoule energy consumption

Emulation of biological synapses is an important step toward construction of large-scale brain-inspired electronics. Despite remarkable progress in emulating synaptic functions, current synaptic devices still consume energy that is orders of magnitude greater than do biological synapses (~10 fJ per synaptic event). Reduction of energy consumption of artificial synapses remains a difficult challenge. We report organic nanowire (ONW) synaptic transistors (STs) that emulate the important working principles of a biological synapse. The ONWs emulate the morphology of nerve fibers. With a core-sheath-structured ONW active channel and a well-confined 300-nm channel length obtained using ONW lithography, ~1.23 fJ per synaptic event for individual ONW was attained, which rivals that of biological synapses. The ONW STs provide a significant step toward realizing low-energy-consuming artificial intelligent electronics and open new approaches to assembling soft neuromorphic systems with nanometer feature size.

Investigation and Manipulation of Different Analog Behaviors of Memristor as Electronic Synapse for Neuromorphic Applications

Low-power and high-density electronic synapse is an important building block of brain-inspired systems. The recent advancement in memristor has provided an opportunity to advance electronic synapse design. However, a guideline on designing and manipulating the memristor’s analog behaviors is still lacking. In this work, we reveal that compliance current (I_comp) of electroforming process played an important role in realizing a stable analog behavior, which is attributed to the generation of conical-type conductive filament. A proper I_comp could result in a large conductance window, good stability, and low voltage analog switching. We further reveal that different pulse conditions can lead to three analog behaviors, where the conductance changes in monotonic increase, plateau after initial jump, and impulse-like shape, respectively. These behaviors could benefit the design of electronic synapse with enriched learning capabilities. This work will provide a useful guideline for designing and manipulating memristor as electronic synapses for brain-inspired systems.

Convertible resistive switching characteristics between memory switching and threshold switching in a single ferritin-based memristor

The coexistence and inter-conversion between threshold and memory resistance switching in a ferritin memristor makes it a promising candidate for physiological applications.