Ultrasensitive Memristive Synapses Based on Lightly Oxidized Sulfide Films

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

Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.     

A MoS2/PTCDA Hybrid Heterojunction Synapse with Efficient Photoelectric Dual Modulation and Versatility

Just as biological synapses provide basic functions for the nervous system, artificial synaptic devices serve as the fundamental building blocks of neuromorphic networks; thus, developing novel artificial synapses is essential for neuromorphic computing. By exploiting the band alignment between 2D inorganic and organic semiconductors, the first multi‐functional synaptic transistor based on a molybdenum disulfide (MoS₂)/perylene‐3,4,9,10‐tetracarboxylic dianhydride (PTCDA) hybrid heterojunction, with remarkable short‐term plasticity (STP) and long‐term plasticity (LTP), is reported. Owing to the elaborate design of the energy band structure, both robust electrical and optical modulation are achieved through carriers transfer at the interface of the heterostructure, which is still a challenging task to this day. In electrical modulation, synaptic inhibition and excitation can be achieved simultaneously in the same device by gate voltage tuning. Notably, a minimum inhibition of 3% and maximum facilitation of 500% can be obtained by increasing the electrical number, and the response to different frequency signals indicates a dynamic filtering characteristic. It exhibits flexible tunability of both STP and LTP and synaptic weight changes of up to 60, far superior to previous work in optical modulation. The fully 2D MoS₂/PTCDA hybrid heterojunction artificial synapse opens up a whole new path for the urgent need for neuromorphic computation devices.     

Synaptic Barristor Based on Phase‐Engineered 2D Heterostructures

The development of energy‐efficient artificial synapses capable of manifoldly tuning synaptic activities can provide a significant breakthrough toward novel neuromorphic computing technology. Here, a new class of artificial synaptic architecture, a three‐terminal device consisting of a vertically integrated monolithic tungsten oxide memristor, and a variable‐barrier tungsten selenide/graphene Schottky diode, termed as a ‘synaptic barrister,’ are reported. The device can implement essential synaptic characteristics, such as short‐term plasticity, long‐term plasticity, and paired‐pulse facilitation. Owing to the electrostatically controlled barrier height in the ultrathin van der Waals heterostructure, the device exhibits gate‐controlled memristive switching characteristics with tunable programming voltages of 0.2?0.5 V. Notably, by electrostatic tuning with a gate terminal, it can additionally regulate the degree and tuning rate of the synaptic weight independent of the programming impulses from source and drain terminals. Such gate tunability cannot be accomplished by previously reported synaptic devices such as memristors and synaptic transistors only mimicking the two‐neuronal‐based synapse. These capabilities eventually enable the accelerated consolidation and conversion of synaptic plasticity, functionally analogous to the synapse with an additional neuromodulator in biological neural networks.     

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.     

Biological Spiking Synapse Constructed from Solution Processed Bimetal Core–Shell Nanoparticle Based Composites

Inspired by the highly parallel processing power and low energy consumption of the biological nervous system, the development of a neuromorphic computing paradigm to mimic brain‐like behaviors with electronic components based artificial synapses may play key roles to eliminate the von Neumann bottleneck. Random resistive access memory (RRAM) is suitable for artificial synapse due to its tunable bidirectional switching behavior. In this work, a biological spiking synapse is developed with solution processed Au@Ag core–shell nanoparticle (NP)‐based RRAM. The device shows highly controllable bistable resistive switching behavior due to the favorable Ag ions migration and filament formation in the composite film, and the good charge trapping and transport property of Au@Ag NPs. Moreover, comprehensive synaptic functions of biosynapse including paired‐pulse depression, paired‐pulse facilitation, post‐tetanic potentiation, spike‐time‐dependent plasticity, and the transformation from short‐term plasticity to long‐term plasticity are emulated. This work demonstrates that the solution processed bimetal core–shell nanoparticle‐based biological spiking synapse provides great potential for the further creation of a neuromorphic computing system.     

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.     

Photonic Potentiation and Electric Habituation in Ultrathin Memristive Synapses Based on Monolayer MoS2

Monolayer of 2D transition metal dichalcogenides, with a thickness of less than 1 nm, paves a feasible path to the development of ultrathin memristive synapses, to fulfill the requirements for constructing large‐scale high density 3D stacking neuromorphic chips. Herein, memristive devices based on monolayer n‐MoS₂ on p‐Si substrate with a large self‐rectification ratio, exhibiting photonic potentiation and electric habituation, are successfully fabricated. Versatile synaptic neuromorphic functions, such as potentiation/habituation, short‐term/long‐term plasticity, and paired‐pulse facilitation, are successfully mimicked based on the inherent persistent photoconductivity performance and the volatile resistive switching behavior. These findings demonstrate the potential applications of ultrathin transition metal dichalcogenides for memristive synapses. These memristive synapses with the combination of photonic and electric neuromorphic functions have prospective in the applications of synthetic retinas and optoelectronic interfaces for integrated photonic circuits based on mixed‐mode electro‐optical operation.     

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

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

Mimicking Synaptic Plasticity and Neural Network Using Memtranstors

Artificial synaptic devices that mimic the functions of biological synapses have drawn enormous interest because of their potential in developing brain‐inspired computing. Current studies are focusing on memristive devices in which the change of the conductance state is used to emulate synaptic behaviors. Here, a new type of artificial synaptic devices based on the memtranstor is demonstrated, which is a fundamental circuit memelement in addition to the memristor, memcapacitor, and meminductor. The state of transtance (presented by the magnetoelectric voltage) in memtranstors acting as the synaptic weight can be tuned continuously with a large number of nonvolatile levels by engineering the applied voltage pulses. Synaptic behaviors including the long‐term potentiation, long‐term depression, and spiking‐time‐dependent plasticity are implemented in memtranstors made of Ni/0.7Pb(Mg_1/3Nb_2/3)O₃‐0.3PbTiO₃/Ni multiferroic heterostructures. Simulations reveal the capability of pattern learning in a memtranstor network. The work elucidates the promise of memtranstors as artificial synaptic devices with low energy consumption.