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

Improvement of volatile switching in scaled silicon nanofin memristor for high performance and efficient reservoir computing

Conductive-bridge random access memory can be used as a physical reservoir for temporal learning in reservoir computing owing to its volatile nature. Herein, a scaled Cu/HfOx/n+-Si memristor was fabricated and characterized for reservoir computing. The scaled, silicon nanofin bottom electrode formation is verified by scanning electron and transmission electron microscopy. The scaled device shows better cycle-to-cycle switching variability characteristics compared with those of large-sized cells. In addition, synaptic characteristics such as conductance changes due to pulses, paired-pulse facilitation, and excitatory postsynaptic currents are confirmed in the scaled memristor. High-pattern accuracy is demonstrated by deep neural networks applied in neuromorphic systems in conjunction with the use of the Modified National Institute of Standards and Technology database. Furthermore, a reservoir computing system is introduced with six different states attained by adjusting the amplitude of the input pulse. Finally, high-performance and efficient volatile reservoir computing in the scaled device is demonstrated by conductance control and system-level reservoir computing simulations.

Optoelectronic Synapse Behaviors in Tb3+ and Al3+ Co-Doped CaSnO3 with Long-Persistent Luminescence

Neuromorphic computation draws inspiration from the remarkable features of the human brain including low energy consumption, parallelism, adaptivity, cognitive functions, and learning ability. These qualities hold the promise of unlocking groundbreaking computational techniques that surpass the limitations of traditional computing systems. This paper reports a remarkable photo-synaptic behavior in the field of rare earth ion-doped luminescent oxides by using long-persistent luminescence (LPL). This system utilizes electron trap states to regulate the synaptic behavior, operating through a fundamentally different mechanism from that of electronic-based synaptic devices. To realize this strategy, Tb3+ doped CaSnO3, which shows a significant LPL property under UV-light excitation, is prepared. The luminescent system shows key neuromorphic characteristics such as paired-pulse facilitation, pulse-number/timing dependent potentiation, and pulse-number/timing dependent short- to long-term plasticity transition, which are required for realizing synaptic devices. This feature expands the way for advanced neuromorphic technologies employing light stimuli.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.

Low Power Volatile and Nonvolatile Memristive Devices from 1D MoO2-MoS2 Core-Shell Heterostructures for Future Bio-Inspired Computing

Memristors-based integrated circuits for emerging bio-inspired computing paradigms require an integrated approach utilizing both volatile and nonvolatile memristive devices. Here, an innovative architecture comprising of 1D CVD-grown core-shell heterostructures (CSHSs) of MoO2-MoS2 is employed as memristors manifesting both volatile switching (with high selectivity of 107 and steep slope of 0.6 mV decade-1) and nonvolatile switching phenomena (with Ion/Ioff ≈103 and switching speed of 60 ns). In these CSHSs, the metallic core MoO2 with high current carrying capacity provides a conformal and immaculate interface with semiconducting MoS2 shells and therefore it acts as a bottom electrode for the memristors. The power consumption in volatile devices is as low as 50 pW per set transition and 0.1 fW in standby mode. Voltage-driven current spikes are observed for volatile devices while with nonvolatile memristors, key features of a biological synapse such as short/long-term plasticity and paired pulse facilitation are emulated suggesting their potential for the development of neuromorphic circuits. These CSHSs offer an unprecedented solution for the interfacial issues between metallic electrodes and the layered materials-based switching element with the prospects of developing smaller footprint memristive devices for future integrated circuits.

Monolithic 2D Perovskites Enabled Artificial Photonic Synapses for Neuromorphic Vision Sensors

Neuromorphic visual sensors (NVS) based on photonic synapses hold a significant promise to emulate the human visual system. However, current photonic synapses rely on exquisite engineering of the complex heterogeneous interface to realize learning and memory functions, resulting in high fabrication cost, reduced reliability, high energy consumption and uncompact architecture, severely limiting the up-scaled manufacture, and on-chip integration. Here a photo-memory fundamental based on ion-exciton coupling is innovated to simplify synaptic structure and minimize energy consumption. Due to the intrinsic organic/inorganic interface within the crystal, the photodetector based on monolithic 2D perovskite exhibits a persistent photocurrent lasting about 90 s, enabling versatile synaptic functions. The electrical power consumption per synaptic event is estimated to be≈1.45 × 10-16 J, one order of magnitude lower than that in a natural biological system. Proof-of-concept image preprocessing using the neuromorphic vision sensors enabled by photonic synapse demonstrates 4 times enhancement of classification accuracy. Furthermore, getting rid of the artificial neural network, an expectation-based thresholding model is put forward to mimic the human visual system for facial recognition. This conceptual device unveils a new mechanism to simplify synaptic structure, promising the transformation of the NVS and fostering the emergence of next generation neural networks.

Volatile threshold switching and synaptic properties controlled by Ag diffusion using Schottky defects

We investigated diffusion memristors in the structure of Ag/Ta2O5/HfO2/Pt, in which active Ag ions control active metal ion diffusion and mimic biological brain functions. The CMOS compatible high-k metal oxide could control an Ag electrode that was ionized by applying an appropriate voltage to form a conductive filament, and the movement of Ag ions was chemically and electrically controlled due to oxygen density. This diffusion memristor exhibited diffused characteristics with a selectivity of 109, and achieved a low power consumption of 2 mW at a SET voltage of 0.2 V. The threshold transitions were reliably repeatable over 20 cycles for compliance currents of 10-6 A, 10-4 A, and no compliance current, with the largest standard deviation value of SET variation being 0.028. Upon filament formation, Ag ions readily diffused into the interface of the Ta2O5 and HfO2 layer, which was verified by investigating the Ag atomic percentage using XPS and vertical EDX and by measuring the relaxation time of 0.8 ms. Verified volatile switching device demonstrated the biological synaptic properties of quantum conductance, short-term memory, and long-term memory due to controlling the Ag. Diffusion memristors using designed control and switching layers as following film density and oxygen vacancy have improved results as low-power devices and neuromorphic devices compared to other diffusion-based devices, and these properties can be used for various applications such as selectors, synapses, and neuromorphic devices.

Vacancy-Engineered Nickel Ferrite Forming-Free Low-Voltage Resistive Switches for Neuromorphic Circuits

Innovations in resistive switching devices constitute a core objective for the development of ultralow-power computing devices. Forming-free resistive switching is a type of resistive switching that eliminates the need for an initial high voltage for the formation of conductive filaments and offers promising opportunities to overcome the limitations of traditional resistive switching devices. Here, we demonstrate mixed charge state oxygen vacancy-engineered electroforming-free resistive switching in NiFe2O4 (NFO) thin films, fabricated as asymmetric Ti/NFO/Pt heterostructures, for the first time. Using pulsed laser deposition in a controlled oxygen atmosphere, we tune the oxygen vacancies together with the cationic valence state in the nickel ferrite phase, with the latter directly affecting the charge state of the oxygen vacancies. The structural integrity and chemical composition of the films are confirmed by X-ray diffraction and hard X-ray photoelectron spectroscopy, respectively. Electrical transport studies reveal that resistive switching characteristics in the films can be significantly altered by tuning the amount and charge state of the oxygen vacancy concentration during the deposition of the films. The resistive switching mechanism is seen to depend upon the migration of both singly and doubly charged oxygen vacancies formed as a result of changes in the nickel valence state and the consequent formation/rupture of conducting filaments in the switching layer. This is supported by the existence of an optimum oxygen vacancy concentration for efficient low-voltage resistive switching, below or above which the switching process is inhibited. Along with the filamentary switching mechanism, the Ti top electrode also enhances the resistive switching performance due to interfacial effects. Time-resolved measurements on the devices display both long- and short-term potentiation in the optimized vacancy-engineered NFO resistive switches, ideal for solid-state synapses achieved in a single system. Our work on correlated oxide forming-free resistive switches holds significant potential for CMOS-compatible low-power, nonvolatile resistive memory and neuromorphic circuits.

MoOx Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware

Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoOx memristors that exhibit nonvolatile ultrafast switching (0.6-0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>104), a long durability (>104 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.

Neuromorphic Computing of Optoelectronic Artificial BFCO/AZO Heterostructure Memristors Synapses

Compared with purely electrical neuromorphic devices, those stimulated by optical signals have gained increasing attention due to their realistic sensory simulation. In this work, an optoelectronic neuromorphic device based on a photoelectric memristor with a Bi2FeCrO6/Al-doped ZnO (BFCO/AZO) heterostructure is fabricated that can respond to both electrical and optical signals and successfully simulate a variety of synaptic behaviors, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analyzing the energy band structures of AZO and BFCO. A convolutional neural network (CNN) architecture for pattern classification at the Mixed National Institute of Standards and Technology (MNIST) was used and improved the recognition accuracy of the MNIST and Fashion-MNIST datasets to 95.21% and 74.19%, respectively, by implementing an improved stochastic adaptive algorithm. These results provide a feasible approach for future implementation of optoelectronic synapses.

Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity

Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed via the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching (Vth ∼ 1-2 V) with an ultra-low energy requirement of ∼1.3 fJ per synaptic event. Several configurations of the order of hierarchy in the device architecture are studied comprehensively to identify the importance of the individual metallic components in contributing to the threshold switching and energy-minimization. The emerging potentiation behavior of the conductance (G) profile under electrical stimulation and its permanence beyond are realized over a wide current compliance range of 0.25 to 300 μA, broadly classifying the short- and long-term potentiation grounded on the characteristics of filamentary structures. The scale-free correlation of potentiation in the device hosting metallic filaments of diverse shapes and strengths could provide an ideal platform for understanding and replicating the complex behavior of the brain for neuromorphic computing.

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.

Superior artificial synaptic properties applicable to neuromorphic computing system in HfOx-based resistive memory with high recognition rates

With the development of artificial intelligence and the importance of big data processing, research is actively underway to break away from data bottlenecks and modern Von Neumann architecture computing structures that consume considerable energy. Among these, hardware technology for neuromorphic computing is in the spotlight as a next-generation intelligent hardware system because it can efficiently process large amounts of data with low power consumption by simulating the brain’s calculation algorithm. In addition to memory devices with existing commercial structures, various next-generation memory devices, including memristors, have been studied to implement neuromorphic computing. In this study, we evaluated the synaptic characteristics of a resistive random access memory (ReRAM) with a Ru/HfOx /TiN structure. Under a series of presynaptic spikes, the device successfully exhibited remarkable long-term plasticity and excellent nonlinearity properties. This synaptic device has a high operating speed (20 ns, 50 ns), long data retention time (> 2 h @85 ℃) and high recognition rate (94.7%). Therefore, we propose that memory and learning capabilities can be used as promising HfOx -based memristors in next-generation artificial neuromorphic computing systems.

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications

Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

A Reconfigurable Optoelectronic Synaptic Transistor with Stable Zr-CsPbI3 Nanocrystals for Visuomorphic Computing

Reconfigurable phototransistor memory attracts considerable attention for adaptive visuomorphic computing, with highly efficient sensing, memory, and processing functions integrated onto a single device. However, developing reconfigurable phototransistor memory remains a challenge due to the lack of an all-optically controlled transition between short-term plasticity (STP) and long-term plasticity (LTP). Herein, an air-stable Zr-CsPbI₃ perovskite nanocrystal (PNC)-based phototransistor memory is designed, which is capable of broadband photoresponses. Benefitting from the different electron capture ability of Zr-CsPbI₃ PNCs to 650 and 405 nm light, an artificial synapse and non-volatile memory can be created on-demand and quickly reconfigured within a single device for specific purposes. Owing to the optically reconfigurable and wavelength-aware operation between STP and LTP modes, the integrated blue feature extraction and target recognition can be demonstrated in a homogeneous neuromorphic vision sensor array. This work suggests a new way in developing perovskite optoelectronic transistors for highly efficient in-sensor computing.

Recent advances in exploiting carrageenans as a versatile functional material for promising biomedical applications

Carrageenans are a group of biopolymers widely found in red seaweeds. Commercial carrageenans have been traditionally used as emulsifiers, stabilizers, and thickening and gelling agents in food products. Carrageenans are regarded as bioactive polysaccharides with disease-modifying and microbiota-modulating activities. Novel biomedical applications of carrageenans as biocompatible functional materials for fabricating hydrogels and nanostructures, including carbon dots, nanoparticles, and nanofibers, have been increasingly exploited. In this review, we describe the unique structural characteristics of carrageenans and their functional relevance. We summarize salient physicochemical features, including thixotropic and shear-thinning properties, of carrageenans. Recent results from clinical trials in which carrageenans were applied as both antiviral and antitumor agents and functional materials are discussed. We also highlight the most recent advances in the development of carrageenan-based targeted drug delivery systems with various pharmaceutical formulations. Promising applications of carrageenans as a bioink material for 3D printing in tissue engineering and regenerative medicine are systematically evaluated. We envisage some key hurdles and challenges in the commercialization of carrageenans as a versatile material for clinical practice. This comprehensive review of the intimate relationships among the structural features, unique rheological properties, and biofunctionality of carrageenans will provide novel insights into their biomedicine application potential.

Amyloid-Gold Nanoparticle Hybrids for Biocompatible Memristive Devices

Biomolecular materials offer tremendous potential for the development of memristive devices due to their low cost of production, environmental friendliness, and, most notably, biocompatibility. Herein, biocompatible memristive devices based on amyloid-gold nanoparticle hybrids have been investigated. These memristors demonstrate excellent electrical performance, featuring an ultrahigh R_off/R_on ratio (>10⁷), a low switching voltage (<0.8 V), and reliable reproducibility. Additionally, the reversible transition from threshold switching to resistive switching mode was achieved in this work. The arrangement of peptides in amyloid fibrils endows the surface polarity and phenylalanine packing, which provides channels for the migration of Ag ions in the memristors. By modulating voltage pulse signals, the study successfully imitates the synaptic behavior of excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). More interestingly, Boolean logic standard cells were designed and simulated using the memristive devices. The fundamental and experimental results of this study thus offer insights into the utilization of biomolecular materials for advanced memristive devices.

Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.

Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems

Natural organic materials such as protein and carbohydrates are abundant in nature, renewable, and biodegradable, desirable for the construction of artificial synaptic devices for emerging neuromorphic computing systems with energy efficient operation and environmentally friendly disposal. These artificial synaptic devices are based on memristors or transistors with the memristive layer or gate dielectric formed by natural organic materials. The fundamental requirement for these synaptic devices is the ability to mimic the memory and learning behaviors of biological synapses. This paper reviews the synaptic functions emulated by a variety of artificial synaptic devices based on natural organic materials and provides a useful guidance for testing and investigating more of such devices.

Forming-Free Tunable Analog Switching in WOx/TaOx Heterojunction for Emulating Electronic Synapses

In this work, the sputtered deposited WO_x/TaO_x switching layer has been studied for resistive random-access memory (RRAM) devices. Gradual SET and RESET behaviors with reliable device-to-device variability were obtained with DC voltage sweep cycling without an electroforming process. The memristor shows uniform switching characteristics, low switching voltages, and a high R_ON/R_OFF ratio (~10²). The transition from short-term plasticity (STP) to long-term potentiation (LTP) can be observed by increasing the pulse amplitude and number. Spike-rate-dependent plasticity (SRDP) and paired-pulse facilitation (PPF) learning processes were successfully emulated by sequential pulse trains. By reducing the pulse interval, the synaptic weight change increases due to the residual oxygen vacancy near the conductive filaments (CFs). This work explores mimicking the biological synaptic behavior and further development for next-generation neuromorphic applications.

Improved Resistive Switching with Low-Power Synaptic Behaviors of ZnO/Al2O3 Bilayer Structure

In this work, the resistive switching behavior of bilayer ZnO/Al₂O₃-based resistive-switching random access memory (RRAM) devices is demonstrated. The polycrystalline nature of the ZnO layer confirms the grain boundary, which helps easy oxygen ion diffusion. Multilevel resistance states were modulated under DC bias by varying the current compliance from 0.1 mA to 0.8 mA, the SET operations where the low resistance state of the memristor device was reduced from 25 kΩ to 2.4 kΩ. The presence of Al₂O₃ acts as a redox layer and facilitates oxygen vacancy exchange that demonstrates stable gradual conductance change. Stepwise disruption of conductive filaments was monitored depending on the slow DC voltage sweep rate. This is attributed to the atomic scale modulation of oxygen vacancies with four distinct reproducible quantized conductance states, which shows multilevel data storage capability. Moreover, several crucial synaptic properties such as potentiation/depression under identical presynaptic pulses and the spike-rate-dependent plasticity were implemented on ITO/ZnO/Al₂O₃/TaN memristor. The postsynaptic current change was monitored defining the long-term potentiation by increasing the presynaptic stimulus frequency from 5 Hz to 100 Hz. Moreover, the repetitive pulse voltage stimulation transformed the short-term plasticity to long-term plasticity during spike-number-dependent plasticity.

Biopolymer based artificial synapses enable linear conductance tuning and low-power for neuromorphic computing

Neuromorphic computing is considered a promising method for resolving the traditional von Neumann bottleneck. Natural biomaterial-based artificial synapses are popular units for constructing neuromorphic computing systems while suffering from poor linearity and limited conduction states. In this work, a AgNO₃ doped iota-carrageenan (ι-car) based memristor is proposed to resolve the non-linear limitation. The memristor presents linear conductance tuning with a higher endurance (∼10⁴), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO₃ is doped to ι-car to suppress the formation of Ag filaments, thereby eliminating uneven Joule heating. Using deep learning of hand-written digits as an application, a doping-enhanced recognition accuracy (93.8%) is achieved, close to that of an ideal synaptic device (95.7%). This work verifies the feasibility of using biopolymers for future high-performance computational and wearable/implantable electronic applications.

Perspective on Nanofluidic Memristors: from Mechanism to Application

Nanofluidic memristors are memory resistors based on nanoconfined fluidic systems exhibiting history-dependent ion conductivity. Toward establishing powerful computing systems beyond the Harvard architecture, these ion-based neuromorphic devices attracted enormous research attention owing to the unique characteristics of ion-based conductors. However, the design of nanofluidic memristor is still at a primary state and a systematic guidance on the rational design of nanofluidic system is desperately required for the development of nanofluidic-based neuromorphic devices. Herein, we proposed a systematic review on the history, main mechanism and potential application of nanofluidic memristors in order to give a prospective view on the design principle of memristors based on nanofluidic systems. Furthermore, based on the present status of these devices, some fundamental challenges for this promising area were further discussed to show the possible application of these ion-based devices.

Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception

Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.

Metal Halide Perovskite-Based Memristors for Emerging Memory Applications

There is an increased demand for next-generation memory devices with high density and fast operation speed to replace conventional memory devices. Memristors are promising candidates for next-generation memory devices because of their scalability, stable data retention, low power consumption, and fast operation. Among the various types of memristors, halide perovskites exhibit potential as emerging materials for memristors by using hysteresis based on the movement of defects or ions in halide perovskites. However, research on the implementation of perovskite materials as memristors is in its early stages; some challenges and problems must be solved to enable the practical application of halide perovskites for next-generation memory devices. From this perspective, we highlight the recent progress in memristors that use halide perovskites. Moreover, we introduce a strategy to enhance the performance and analyze the operation mechanism of memory devices that use halide perovskites. Finally, we summarize the challenges in the development of device technology to use halide perovskites in next-generation memory devices.

Synaptic Current Response of a Liquid Ga Electrode via a Surface Electrochemical Redox Reaction in a NaOH Solution

An ionic device using a liquid Ga electrode in a 1 M NaOH solution is proposed to generate artificial neural spike signals. The oxidation and reduction at the liquid Ga surface were investigated for different bias voltages at 50 °C. When the positive sweep voltage from the starting voltage (V _S) of 1 V was applied to the Ga electrode, the oxidation current flowed immediately and decreased exponentially with time. The spike and decay current behavior resembled the polarization and depolarization at the influx and extrusion of Ca²⁺ in biological synapses. Different average decay times of ∼81 and ∼310 ms were implemented for V _S of -2 and -5 V, respectively, to mimic the synaptic responses to short- and long-term plasticity; these decay states can be exploited for application in binary electrochemical memory devices. The oxidation mechanism of liquid Ga was studied. The differences in Ga ion concentration due to V _S led to differences in oxidation behavior. Our device is beneficial for the organ cell-machine interface system because liquid Ga is biocompatible and flexible; thus, it can be applied in biocompatible and flexible neuromorphic device development for neuroprosthetics, human cell-machine interface formation, and personal health care monitoring.

Synaptic Plasticity and Quantized Conductance States in TiN-Nanoparticles-Based Memristor for Neuromorphic System

Controlled conductive filament formation in the resistive random access memory device is an essential requirement for analog resistive switching to develop artificial synapses. In this work, we have studied Au/Ti/HfAlO_x/TiN-NP/HfAlO_x/ITO RRAM device to demonstrate conductance quantization behavior to achieve the high-density memory application. Stepwise change in conductance under DC and pulse voltage confirms the quantized conductance states with integer and half-integer multiples of G₀. Reactive TiN-NPs inside the switching layer helps to form and rupture the atomic scale conductive filaments due to enhancing the local electric field inside. Bipolar resistive switching characteristics at low SET/RESET voltage were obtained with memory window > 10 and stable endurance of 10³ cycles. Short-term and long-term plasticities are successfully demonstrated by modulating the pre-spike number, magnitude, and frequency. The quantized conductance behavior with promising synaptic properties obtained in the experiments suggests HfAlO_x/TiN-NP/HfAlO_x switching layer is suitable for multilevel high-density storage RRAM devices.

A Modified SiO2-Based Memristor with Reliable Switching and Multifunctional Synaptic Behaviors

Dielectric SiO₂ has possible uses as an active layer for emerging memory due to its high on/off ratio and low operation voltage. However, SiO₂-based memory that relies on the conducting filament still has limited endurance and stability. Here, we have constructed a passivated layer of SiO₂ using Ag-doped SrTiO₃, which serves as a Ag ion reservoir for the control of filament formation. It is demonstrated that the modified memristor presents an excellent endurance switching and could stably be operated in an ambient environment for 20 days without visible degradation. Based on the reliable switching, the synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, transition from short-term memory to long-term memory, and potentiation/depression have also been implemented. Furthermore, a 7 × 7 pixel array made from memristors has successfully mimicked simple learning and forgetting behavior. The experimental results offer an alternative approach for SiO₂-based memristors and a possibility to be applied in neuromorphic computing.

Evolution and modulation of Ag filament dynamics within memristive devices based on necklace-like Ag@TiO2nanowire networks

Random nanowire networks (NWNs) are regarded as promising memristive materials for applications in information storage, selectors, and neuromorphic computing. The further insight to understand their resistive switching properties and conduction mechanisms is crucial to realize the full potential of random NWNs. Here, a novel planar memristive device based on necklace-like structure Ag@TiO₂NWN is reported, in which a strategy only using water to tailor the TiO₂shell on Ag core for necklace-like core-shell structure is developed to achieve uniform topology connectivity. With analyzing the influence of compliance current on resistive switching characteristics and further tracing evolution trends of resistance state during the repetitive switching cycles, two distinctive evolution trends of low resistance state failure and high resistance state failure are revealed, which bear resemblance to memory loss and consolidation in biological systems. The underlying conduction mechanisms are related to the modulation of the Ag accumulation dynamics inside the filaments at cross-point junctions within conductive paths of NWNs. An optimizing principle is then proposed to design reproducible and reliable threshold switching devices by tuning the NWN density and electrical stimulation. The optimized threshold switching devices have a high ON/OFF ratio of ∼10⁷with threshold voltage as low as 0.35 V. This work will provide insights into engineering random NWNs for diverse functions by modulating external excitation and optimizing NWN parameters to satisfy specific applications, transforming from neuromorphic systems to threshold switching devices as selectors.

Analytically and empirically consistent characterization of the resistive switching mechanism in a Ag conducting-bridge random-access memory device through a pseudo-liquid interpretation approach

A new physical analysis of the filament formation in a Ag conducting-bridge random-access memory (CBRAM) device in consideration of the existence of inter-atomic attractions caused by metal bonding is suggested. The movement of Ag atoms inside the switching layer is characterized hydrodynamically using the Young-Laplace equation during set and reset operations. Both meridional and azimuthal curvatures of the Ag filament protruding from the Ag electrode are accurately calculated to track down the exact shape of the Ag filament with change in the applied voltage. The second-order partial differential equation for the Ag filament geometry is derived from the equation of equilibrium between the electrostatic pressure and the Laplace one. The solution to the equation is numerically obtained, and furthermore, the abrupt set operation in the forming process, bipolar resistive-switching, and the threshold switching operation in the reset operations are successfully simulated in accordance with the numerical solutions. Also, it is demonstrated that the currents extracted from the suggested model show good agreement with the I-V characteristics measured from the fabricated Ag CBRAM device.

Flexible and recyclable bio-based transient resistive memory enabled by self-healing polyimine membrane

The recyclable, self-healing and easily-degradable transient electronic technology has aroused tremendous attention in flexible electronic products. However, integrating the above advantages into one single flexible electronic device is still a huge challenge. Herein, we demonstrate a flexible and recyclable bio-based memory device using fish colloid as the resistive switching layer on a polyimine substrate, which affords reliable mechanical and electrical properties under repetitive conformal deformation operation. This flexible bio-based memory device presents potential analog behaviors including memory characteristics and excitatory current response, which undergoes incremental potentiation in conductance under successive electrical pulses. Moreover, this device is expected to greatly alleviate the environmental problems caused by electronic waste. It can be decomposed rapidly in water and well recycled, which is a promising candidate for transient memories and information security. We believe that this study can provide new possibilities to the field of high-performance transient electronics and flexible resistive memory devices.

Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs

Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO₃ (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input.

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.

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.

Emulation of biphasic plasticity in retinal electrical synapses for light-adaptive pattern pre-processing

Electrical synapses provide rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on second-order conductance transition (SOCT) in an Ag-based memristor for the first time. High-resolution transmission electron microscopy indicates that SOCT is mediated by the virtual silver electrode. Besides the conventional chemical synaptic behaviors, the biphasic plasticity of electrical synapses is well emulated by integrating the device with a photosensitive element to form an optical pre-processing unit (OPU), which contributes to the retinal neural circuitry and is adaptive to ambient illumination. By synergizing the OPU and spiking neural network (SNN), adaptive pattern recognition tasks are accomplished under different light and noise settings. This work not only contributes to the further completion of synaptic behaviour for hardware-level neuromorphic computing, but also potentially enables image pre-processing with light adaptation and noise suppression for adaptive visual recognition.

Solution-processed electronics for artificial synapses

Artificial synaptic devices and systems have become hot topics due to parallel computing, high plasticity, integration of storage, and processing to meet the challenges of the traditional Von Neumann computers. Currently, two-terminal memristors and three-terminal transistors have been mainly developed for high-density storage with high switching speed and high reliability because of the adjustable resistivity, controllable ion migration, and abundant choices of functional materials and fabrication processes. To achieve the low-cost, large-scale, and easy-process fabrication, solution-processed techniques have been extensively employed to develop synaptic electronics towards flexible and highly integrated three-dimensional (3D) neural networks. Herein, we have summarized and discussed solution-processed techniques in the fabrication of two-terminal memristors and three-terminal transistors for the application of artificial synaptic electronics mainly reported in the recent five years from the view of fabrication processes, functional materials, electronic operating mechanisms, and system applications. Furthermore, the challenges and prospects were discussed in depth to promote solution-processed techniques in the future development of artificial synapse with high performance and high integration.

Electrical Properties and Biological Synaptic Simulation of Ag/MXene/SiO2/Pt RRAM Devices

Utilizing electronic devices to emulate biological synapses for the construction of artificial neural networks has provided a feasible research approach for the future development of artificial intelligence systems. Until now, different kinds of electronic devices have been proposed in the realization of biological synapse functions. However, the device stability and the power consumption are major challenges for future industrialization applications. Herein, an electronic synapse of MXene/SiO2 structure-based resistive random-access memory (RRAM) devices has been designed and fabricated by taking advantage of the desirable properties of SiO2 and 2D MXene material. The proposed RRAM devices, Ag/MXene/SiO2/Pt, exhibit the resistance switching characteristics where both the volatile and nonvolatile behaviors coexist in a single device. These intriguing features of the Ag/MXene/SiO2/Pt devices make them more applicable for emulating biological synaptic plasticity. Additionally, the conductive mechanisms of the Ag/MXene/SiO2/Pt RRAM devices have been discussed on the basis of our experimental results.

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.

Investigating the origins of ultra-short relaxation times of silver filaments in forming-free SiO2-based conductive bridge memristors

The threshold switching effect is considered of outmost importance for a variety of applications ranging from the reliable operation of crossbar architectures to emulating neuromorphic properties with artificial neural networks. This property is strongly believed to be associated with the rich inherit dynamics of a metallic conductive filament (CF) formation and its respective relaxation processes. Understanding the origin of these dynamics is very important in order to control the degree of volatility and design novel electronic devices. Here, we present a synergistic numerical and experimental approach in order to deal with that issue. The distribution of relaxation time is addressed through time-resolved pulse measurements whereas the entire switching behavior is modeled through a 2D dynamical model by taking into account the destructive interference of the drift/diffusion transport mechanisms and the Soret diffusion flux due to the intense local Joule heating. The proposed mechanism interprets successfully both the threshold to bipolar switching transition as well as the self-rectifying effects in SiO₂-based memories. The model incorporates the effect of electrode materials on the switching pattern and provides a different perception of the ionic transport processes, shading light into the ultra-small lifetimes of the CF and explaining the different behavior of the silver or copper active materials in a conductive bridge random access memory architecture.

Bienenstock-Cooper-Munro Learning Rule Realized in Polysaccharide-Gated Synaptic Transistors with Tunable Threshold

With reference to the organization of the human brain nervous system, a hardware-based approach that builds massively parallel neuromorphic circuits is of great significance to neuromorphic computing. The Bienenstock-Cooper-Munro (BCM) learning rule, which describes that the synaptic weight modulation exhibits frequency-dependent and tunable frequency threshold characteristics, is more compatible with the working principle of neuromorphic computing systems than spike-timing-dependent plasticity. Therefore, it is interesting to simulate the BCM learning rule on solid-state synaptic devices. Here, we have prepared λ-carrageenan (λ-car) electrolyte-gated oxide synaptic transistors, which exhibit good transistor performances, including a low subthreshold swing of 125 mV/dec, an on/off ratio larger than 10⁶, and a mobility of 9.5 cm² V^-1 s^-1. By modulating the initial channel current and spike frequency, the simulation of the BCM rule was successfully realized. The competitive relationship between the drift of protons under an electric field and the spontaneous diffusion of protons can explain this mechanism. The proposed λ-car-gated synaptic transistor has a great significance to neuromorphic computing.

Perovskite/Organic Semiconductor-Based Photonic Synaptic Transistor for Artificial Visual System

Artificial visual system with information sensing, processing, and memory function is promoting the development of artificial intelligence techniques. Photonic synapse as an essential component can enhance the visual information processing efficiency owing to the high propagation speed, low latency, and large bandwidth. Herein, photonic synaptic transistors based on organic semiconductor poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno [3,2-b]thiophene)] (DPPDTT) and perovskite CsPbBr₃ quantum dots are fabricated by a simple solution process. The device can simulate fundamental synaptic behaviors, including excitatory postsynaptic current, pair-pulse facilitation, the transition of short-term memory to long-term memory, and "learning experience" behavior. Combining the advantages of the high photosensitivity of perovskites and relatively high conductivity of DPPDTT, the device can exhibit excellent synaptic performances at a low voltage of -0.2 V. Even under an ultralow operation voltage of -0.0005 V, the device can still show obvious synaptic responses. Tunable synaptic integration behaviors including "AND" and "OR" light logic functions can be realized. An artificial visual system is successfully emulated by illuminating the synaptic arrays employing light of different densities. Therefore, low-voltage synaptic devices based on organic semiconductor and CsPbBr₃ quantum dots with a simple fabrication technique present high potential to mimic human visual memory.

Stimuli-Enabled Artificial Synapses for Neuromorphic Perception: Progress and Perspectives

Brain-inspired neuromorphic computing is intended to provide effective emulation of the functionality of the human brain via the integration of electronic components. Recent studies of synaptic plasticity, which represents one of the most significant neurochemical bases of learning and memory, have enhanced the general comprehension of how the brain functions and have thereby eased the development of artificial neuromorphic devices. An understanding of the synaptic plasticity induced by various types of stimuli is essential for neuromorphic system construction. The realization of multiple stimuli-enabled synapses will be important for future neuromorphic computing applications. In this Review, state-of-the-art synaptic devices with particular emphasis on their synaptic behaviors under excitation by a variety of external stimuli are summarized, including electric fields, light, magnetic fields, pressure, and temperature. The switching mechanisms of these synaptic devices are discussed in detail, including ion migration, electron/hole transfer, phase transition, redox-based resistive switching, and other mechanisms. This Review aims to provide a comprehensive understanding of the operating mechanisms of artificial synapses and thus provides the principles required for design of multifunctional neuromorphic systems with parallel processing capabilities.

The coexistence of threshold and memory switching characteristics of ALD HfO2 memristor synaptic arrays for energy-efficient neuromorphic computing

The development of bioinspired electronic devices that can mimic the biological synapses is an essential step towards the development of efficient neuromorphic systems to simulate the functions of the human brain. Among various materials that can be utilized to attain electronic synapses, the existing semiconductor industry-compatible conventional materials are more favorable due to their low cost, easy fabrication and reliable switching properties. In this work, atomic layer deposited HfO2-based memristor synaptic arrays are fabricated. The coexistence of threshold switching (TS) and memory switching (MS) behaviors is obtained by modulating the device current. The TS characteristics are exploited to emulate essential synaptic functions. The Ag diffusive dynamics of our electronic synapses, analogous to the Ca2+ dynamics in biological synapses, is utilized to emulate synaptic functions. Electronic synapses successfully emulate paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), spike-timing-dependent plasticity (STDP), short-term potentiation (STP), long-term potentiation (LTP) and transition from STP to LTP with rehearsals. The psychological memorization model of short-term memory (STM) to long-term memory (LTM) transition is mimicked by image memorization in crossbar array devices. Reliable and repeatable bipolar MS behaviors with a low operating voltage are obtained by a higher compliance current for energy-efficient nonvolatile memory applications.

Solution-processed inorganic δ-phase CsPbI3 electronic synapses with short- and long-term plasticity in a crossbar array structure

Electronic synapses based on memristive devices can potentially open a niche area for neuromorphic computing by replicating the function of biological synapses with high fidelity. Recently, two-terminal memristors based on halide perovskites have demonstrated outstanding memristive properties and a variety of synaptic characteristics, combining with their additional advantages such as a solution-processed fabrication method and low crystalline temperature. However, the concerns over the chemical and phase stability of halide perovskites greatly hinder their practical applications. In this study, by using a simple single-step spin-coating method, we report artificial synapses with superior ambient stability (>90 days under ambient conditions) based on fully inorganic nonperovskite δ-phase CsPbI3 in a cross-bar array architecture. The threshold switching memristive device exhibits a moderate ON/OFF ratio, a relatively low operation voltage (0.3 V) and high endurance (>5 × 105). More importantly, the electronic device can emulate synaptic characteristics such as short-term plasticity, paired-pulse facilitation, and the transition from short-term memory to long-term memory with a high output signal-to-noise ratio (>102). This work represents the first record for artificial synapses based on nonperovskite CsPbI3 and will be a step toward achieving low-cost and high-density practical synapse arrays.

Heterosynaptic Plasticity Emulated by Liquid Crystal-Carbon Nanotube Composites with Modulatory Interneurons

The aim of the neuromorphic computing is to emulate energy-efficient and smart data-processing ability of the biological brain, which is achieved by massively interconnected neurons and synapses. The strength of a connection between two neurons is modified by homosynaptic and heterosynaptic plasticity. As current research in the neuromorphic device is mainly focused on emulating homosynaptic plasticity, complex biological functions are not easy to mimic because they require both homosynaptic and heterosynaptic plasticity. We demonstrate the use of a liquid crystal-carbon nanotube (LC-CNT) composite as a resistive switching material that can emulate both the homosynaptic and heterosynaptic functions of biological neurons. The LC-CNT composite undergoes resistance change by CNT alignment and aggregated wire formation subjected to an applied electric field. A two-terminal device that exploits this mechanism achieves analog switching and homosynaptic potentiation. In a multiterminal device structure, the modulatory interneuron could tune the synaptic properties to perform heterosynaptic functions such as heterosynaptic potentiation, heterosynaptic facilitation, and synaptic weight normalization to emulate complex biological functions of a brain. Artificial synapses that exploit this multifunctionality of the LC-CNT composite have uses in next-generation neuromorphic devices.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices

The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO3-x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks.

Synergistic Improvement of Long-Term Plasticity in Photonic Synapses Using Ferroelectric Polarization in Hafnia-Based Oxide-Semiconductor Transistors

A number of synapse devices have been intensively studied for the neuromorphic system which is the next-generation energy-efficient computing method. Among these various types of synapse devices, photonic synapse devices recently attracted significant attention. In particular, the photonic synapse devices using persistent photoconductivity (PPC) phenomena in oxide semiconductors are receiving much attention due to the similarity between relaxation characteristics of PPC phenomena and Ca²⁺ dynamics of biological synapses. However, these devices have limitations in its controllability of the relaxation characteristics of PPC behaviors. To utilize the oxide semiconductor as photonic synapse devices, relaxation behavior needs to be accurately controlled. In this study, a photonic synapse device with controlled relaxation characteristics by using an oxide semiconductor and a ferroelectric layer is demonstrated. This device exploits the PPC characteristics to demonstrate synaptic functions including short-term plasticity, paired-pulse facilitation (PPF), and long-term plasticity (LTP). The relaxation properties are controlled by the polarization of the ferroelectric layer, and this polarization is used to control the amount by which the conductance levels increase during PPF operation and to enhance LTP characteristics. This study provides an important step toward the development of photonic synapses with tunable synaptic functions.

Threshold-Tunable, Spike-Rate-Dependent Plasticity Originating from Interfacial Proton Gating for Pattern Learning and Memory

Recently, neuromorphic devices have been receiving increasing interest in the field of artificial intelligence (AI). Realization of fundamental synaptic plasticities on hard-ware devices would endow new intensions for neuromorphic devices. Spike-rate-dependent plasticity (SRDP) is one of the most important synaptic learning mechanisms in brain cognitive behaviors. It is thus interesting to mimic the SRDP behaviors on solid-state neuromorphic devices. In the present work, nanogranular phosphorus silicate glass (PSG)-based proton conductive electrolyte-gated oxide neuromorphic transistors have been proposed. The oxide neuromorphic transistors have good transistor performances and frequency-dependent synaptic plasticity behavior. Moreover, the neuromorphic transistor exhibits SRDP activities. Interestingly, by introducing priming synaptic stimuli, the modulation of threshold frequency value distinguishing synaptic potentiation from synaptic depression is realized for the first time on an electrolyte-gated neuromorphic transistor. Such a mechanism can be well understood with interfacial proton gating effects of the nanogranular PSG-based electrolyte. Furthermore, the effects of SRDP learning rules on pattern learning and memory behaviors have been conceptually demonstrated. The proposed neuromorphic transistors have potential applications in neuromorphic engineering.