Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing

The spiking neural network(SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CM...     

All-Optically Controlled Memristor for Optoelectronic Neuromorphic Computing

Neuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh-density 3D integration and their ultralow energy consumption. Compared to traditional electrical memristors, the emerging optoelectronic memristors are more attractive owing to their ability to combine the advantages of both photonics and electronics. However, the inability to reversibly tune the memconductance with light has severely restricted the development of optoelectronic NC. Here, an all-optically controlled (AOC) analog memristor is realized, with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The device is based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light-induced electron trapping and detrapping. It is found that the light-induced multiple memconductance states are nonvolatile. Furthermore, spike-timing-dependent plasticity learning can be mimicked in this AOC memristor, indicating its potential applications in AOC spiking neural networks for highly efficient optoelectronic NC.     

2D Material Based Synaptic Devices for Neuromorphic Computing

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high-performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.     

Boolean Logic Computing Based on Neuromorphic Transistor

General-purpose computers usually use logic gate computing units based on complementary metal oxide semiconductors (CMOS). Due to the separate memory and computing units in Von Neumann architecture, data transmission requires great energy and time consumption. Developing novel neuromorphic devices and comprehensively investigating their logical computing mode are crucial to achieve high-performance and low-power neuromorphic computation. Here, a systematic summary of Boolean logic computing based on emerging neuromorphic transistors is presented. This summary encompasses logical operation modes, materials, device structures, and working mechanisms. The input mode of Boolean logic operation is classified into electrical input, optical input, and synergistic optical/electrical input. Besides, additional modulation strategies to construct programmable logic functions by electrical, optical, and thermal signals are also summarized. These strategies hold great significance as they enable dynamic reconfiguration of logic operations and provide neuromorphic devices with decision-making capabilities. Finally, the application prospects and current challenges to Boolean logic computing based on dendritic integration are discussed from the aspects of device integration, synergistic input/modulation modes, auxiliary peripheral circuit, software/hardware system, etc. It is believed that comprehensive investigations on neuromorphic Boolean logic operations are crucial to push forward the development of future neuromorphic computing toward high efficiency and high integration density.     

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two-Dimensional Materials Integration

In-memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time-consuming challenges associated with the von Neumann architecture. The ferroelectric field-effect transistor (FeFET) technology, with its fast and energy-efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study,  the capabilities of an integrated ferroelectric HfO₂ and 2D MoS₂ channel FeFET in achieving high-performance 4-bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET-based in-memory computing for future neuromorphic computing applications.     

Photoferroelectric Perovskite Synapses for Neuromorphic Computing

Halide perovskite is an emerging material with excellent optoelectronic properties, and also widely used in neuromorphic devices. Recently, halide perovskite has been redefined as exhibiting extraordinary multifunction, e.g., photoferroelectricity. Herein, this work employs a composite material consisting of halide perovskite and organic ferroelectric material to develop a new photoferroelectric synapse, and the photoferroelectricity and some synaptic plasticity are investigated. By the corresponding test analysis, it is demonstrated that photoelectricity and ferroelectricity can reinforce each other in this photoferroelectric composite material. Versatile synaptic behaviors of the nervous system, including paired-pulse facilitation/paired-pulse depression, post-tetanic potentiation /post-tetanic depression, and spiking-rate-dependent plasticity, are successfully simulated. Particularly, the classical conditioning in Pavlov's dog experiment can be replicated in the photoferroelectric synapse to realize the learning function of the brain, including memory loss and recovery. This work could be conducive to the application of multifunctional perovskite materials in synapse devices and neuromorphic computing.     

HfO2-Based Memristor as an Artificial Synapse for Neuromorphic Computing with Tri-Layer HfO2/BiFeO3/HfO2 Design

Neuromorphic devices are among the most emerging electronic components to realize artificial neural systems and replace traditional complementary metal–oxide semiconductor devices in recent times. In this work, tri-layer HfO₂/BiFeO₃(BFO)/HfO₂ memristors are designed by inserting traditional ferroelectric BFO layers measuring ≈4 nm after thickness optimization. The novel designed memristor shows excellent resistive switching (RS) performance such as a storage window of 10⁴ and multi-level storage ability. Remarkably, essential synaptic functions can be successfully realized on the basis of the linearity of conductance modulation. The pattern recognition simulation based on neuromorphic network is conducted with 91.2% high recognition accuracy. To explore the RS performance enhancement and artificial synaptic behaviors, conductive filaments (CFs) composed of Hafnium (Hf) single crystal with a hexaganal lattice structure are observed using high-resolution transmission electron microscopy. It is reasonable to believe that the sufficient oxygen vacancies in the inserting BFO thin film play a crucial role in adjusting the morphology and growth of Hf CFs, which leads to the promising synaptic and enhanced RS behavior, thus demonstrating the potential of this memristor for use in neuromorphic computing.     

Novel Electronics for Flexible and Neuromorphic Computing

Emerging classes of flexible electronic systems that can be attached to a wide range of surfaces from wearable clothes to internal organs have driven significant advances in communication protocols (e.g., Internet of Things, augmented reality) and clinical research, shifting today's personal computing paradigm. The field of “system on plastic” is on the verge of an innovative breakthrough toward a hypercognitive society by being fused with current neuromorphic applications in the spotlight, which can offer intelligent services such as personalized feedback therapy and autonomous driving. The novel concept of electronics for flexible and neuromorphic computing requires an important research leap in micro‐/nanoelectronics on plastics, system‐level integration techniques (interconnection and packaging), and synaptic devices. Here, representative advances and developments in the area of flexible and neuromorphic technologies are reviewed with regard to device configurations, materials, fabrication processes, and their potential research fields.     

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.     

Flexible Memristor Constructed by 2D Cadmium Phosphorus Trichalcogenide for Artificial Synapse and Logic Operation

The development of advanced microelectronics requires new device architecture and multi-functionality. Low-dimensional material is considered as a powerful candidate to construct new devices. In this work, a flexible memristor is fabricated utilizing 2D cadmium phosphorus trichalcogenide nanosheets as the functional layer. The memristor exhibits excellent resistive switching performance under different radius and over 10³ bending times. The device mechanism is systematically investigated, and the synaptic plasticity including paired-pulse facilitation and spiking timing-dependent plasticity are further observed. Furthermore, based on the linearly conductance modulation capacity of the flexible memristor, the applications on decimal operation are explored, that the addition, subtraction, multiplication, and division of decimal calculation are successfully achieved. These results demonstrate the potential of metal phosphorus trichalcogenide in novel flexible neuromorphic devices, which accelerate the application process of neuromorphic computing.     

Non-Volatile Electrolyte-Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low-Power Neuromorphic Computing and Logic-In-Memory

Artificial synapses are the key building blocks for low-power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two-terminal memristors and three-terminal transistors with filament-formation and charge-trapping mechanisms, emerging electrolyte-gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS₂-based EGT is proposed, where an ion-storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li-ion-storage layer, the GDY/MoS₂-based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ µm⁻²), and long retention characteristics (>10⁴ s). In addition, a quasi-linear conductance update with low noise (1.3%), an ultrahigh G_max/G_min ratio (10³), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near-ideal accuracies. Moreover, the non-volatile characteristics of the GDY/MoS₂-based EGT enable it to demonstrate logic-in-memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS₂-based EGT for next-generation low-power electronics beyond von Neumann architecture.     

Advanced Optoelectronic Devices for Neuromorphic Analog Based on Low-Dimensional Semiconductors

Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low-dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light-matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two-terminal neuromorphic memristors, three-terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low-dimensional materials is prospected.     

Bio-Inspired Artificial Perceptual Devices for Neuromorphic Computing and Gesture Recognition

Artificial perception technologies capable of sensing and feeling mechanical stimuli like human skins are critical enablers for electronic skins (E-Skins) needed to achieve artificial intelligence. However, most of the reported electronic skin systems lack the capability to process and interpret the sensor data. Herein, a new design of artificial perceptual system integrating ZnO-based synaptic devices with Pt/carbon nanofibers-based strain sensors for stimuli detection and information processing is presented. Benefiting from the controllable ion migration after indium doping, the device can emulate various essential functions, such as short-term/long-term plasticity, paired-pulse facilitation, excitatory post-synaptic current, and synaptic plasticity depending on the number, frequency, amplitude, and width of the applied pulses. The Pt/carbon nanofibers-based strain sensors can detect subtle human motion and convert mechanical stimuli into electrical signals, which are further processed by the ZnO devices. By attaching the integrated devices to finger joints, it is demonstrated that they can recognize handwriting and gestures with a high accuracy. This work offers new insights in designing artificial synapses and sensors to process and recognize information for neuromorphic computing and artificial intelligence applications.     

Compensated Ferrimagnet Based Artificial Synapse and Neuron for Ultrafast Neuromorphic Computing

Spintronic devices are considered a possible solution for the hardware implementation of artificial synapses and neurons, as a result of their non-volatility, high scalability, complementary metal-oxide-semiconductor transistor compatibility, and low power consumption. As compared to ferromagnets, ferrimagnet-based spintronics exhibits equivalently fascinating properties that have been witnessed in ultrafast spin dynamics, together with efficient electrical or optical manipulation. Their applications in neuromorphic computing, however, have still not been revealed, which motivates the present experimental study. Here, by using compensated ferrimagnets containing Co_0.80Gd_0.20 with perpendicular magnetic anisotropy, it is demonstrated that the behavior of spin-orbit torque switching in compensated ferrimagnets could be used to mimic biological synapses and neurons. In particular, by using the anomalous Hall effect and magneto-optical Kerr effect imaging measurements, the ultrafast stimulation of artificial synapses and neurons is illustrated, with a time scale down to 10 ns. Using experimentally derived device parameters, a three-layer fully connected neural network for handwritten digits recognition is further simulated, based on which, an accuracy of more than 93% could be achieved. The results identify compensated ferrimagnets as an intriguing candidate for the ultrafast neuromorphic spintronics.     

Functional Demonstration of a Memristive Arithmetic Logic Unit (MemALU) for In‐Memory Computing

The development of in‐memory computing has opened up possibilities to build next‐generation non‐von‐Neumann computing architecture. Implementation of logic functions within the memristors can significantly improve the energy efficiency and alleviate the bandwidth congestion issue. In this work, the demonstration of arithmetic logic unit functions is presented in a memristive crossbar with implemented non‐volatile Boolean logic and arithmetic computing. For logic implementation, a standard operating voltage mode is proposed for executing reconfigurable stateful IMP, destructive OR, NOR, and non‐destructive OR logic on both the word and bit lines. No additional voltages are needed beyond “V_P” and its negative component. With these basic logic functions, other Boolean functions are constructed within five devices in at most five steps. For arithmetic computing, the fundamental functions including an n‐bit full adder with high parallelism as well as efficient increment, decrement, and shift operations are demonstrated. Other arithmetic blocks, such as subtraction, multiplication, and division are further designed. This work provides solid evidence that memristors can be used as the building block for in‐memory computing, targeting various low‐power edge computing applications.     

Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three-terminal field-effect transistors with gate-controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber-based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber-based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short-term plasticity behaviors including paired-pulse facilitation, spike duration-dependent plasticity, and dynamic filtering are tuned in this fiber-based device. Furthermore, Perovskite-doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next-generation neuromorphic networks.     

Low Power Computing Paradigms Based on Emerging Non-Volatile Nanodevices

Traditional digital processing approaches are based on semiconductor transistors, which suffer from high power consumption, aggravating with technology node scaling. To solve definitively this problem, a number of emerging non-volatile nanodevices are under intense investigations. Meanwhile, novel computing circuits are invented to dig the full potential of the nanodevices. The combination of non-volatile nanodevices with suitable computing paradigms have many merits compared with the complementary metal-oxide-semiconductor transistor (CMOS) technology based structures, such as zero standby power, ultra-high density, non-volatility, and acceptable access speed. In this paper, we overview and compare the computing paradigms based on the emerging nanodevices towards ultra-low dissipation.     

Artificial Synapses Based on Multiterminal Memtransistors for Neuromorphic Application

Neuromorphic computing, which emulates the biological neural systems could overcome the high‐power consumption issue of conventional von‐Neumann computing. State‐of‐the‐art artificial synapses made of two‐terminal memristors, however, show variability in filament formation and limited capacity due to their inherent single presynaptic input design. Here, a memtransistor‐based arti?cial synapse is realized by integrating a memristor and selector transistor into a multiterminal device using monolayer polycrys‐talline‐MoS₂ grown by a scalable chemical vapor deposition (CVD) process. Notably, the memtransistor offers both drain‐ and gate‐tunable nonvolatile memory functions, which efficiently emulates the long‐term potentiation/depression, spike‐amplitude, and spike‐timing‐dependent plasticity of biological synapses. Moreover, the gate tunability function that is not achievable in two‐terminal memristors, enables significant bipolar resistive states switching up to four orders‐of‐magnitude and high cycling endurance. First‐principles calculations reveal a new resistive switching mechanism driven by the diffusion of double sulfur vacancy perpendicular to the MoS₂ grain boundary, leading to a conducting switching path without the need for a filament forming process. The seamless integration of multiterminal memtransistors may offer another degree‐of‐freedom to tune the synaptic plasticity by a third gate terminal for enabling complex neuromorphic learning.     

Set/Reset Bilaterally Controllable Resistance Switching Ga-doped Ge2Sb2Te5 Long-Term Electronic Synapses for Neuromorphic Computing

Long-term plasticity of bio-synapses modulates the stable synaptic transmission that is quite related to the encoding of information and its emulation using electronic hardware is one of important targets for neuromorphic computing. Ge₂Sb₂Te₅ (GST) based phase change random access memory (PCRAM) has become a strong candidate for complementary-metal-oxide-semiconductor (CMOS) compatible integrated long-term electronic synapses to cope with the high-efficient and low power consumption data processing tasks for neuromorphic computing. However, the performance of PCRAM electronic synapses is still quite limited due to the challenges in linear and continuous conductance regulation, which originates from the fast and uncontrollable resistance switching characteristic of conventional PCRAM for the data storage application. Here an in-depth study is reported on the impact of gallium (Ga) doping on GST (GaGST) structural properties and on the corresponding 0.13 µm CMOS technology fabricated PCRAM integrated devices with a mushroom structure. The Ga doping effectively retarded the crystallization process of GST by augmenting the local disorder of GeTe_4-nGe_n tetrahedron, which subsequently leads to the Set/Reset bilaterally controllable resistance switching of corresponding PCRAM devices. The optimized 6.5%GaGST electronic synapses demonstrate gradual resistance switching characteristics and a good multilevel retention feature and eventually exhibit outstanding long-term synaptic plasticity like potentiation/depression and spiking time dependent plasticity in four forms. Such long-term electronic synapses are applied to handwritten digits recognition (96.22%) and CIFAR-10 image categorization (93.6%) and attain very high accuracy for both tasks. These results provide an effective method to achieve high performance PCRAM electronic synapses and highlight the great potential of GaGST PCRAM as a component for future high-performance neuromorphic computing.     

Functional Materials for Memristor-Based Reservoir Computing: Dynamics and Applications

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high-dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor-implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor-implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on-demand application scenarios. Furthermore, recent advances in the application of memristor-based physical RC systems are surveyed. In the end, the further prospects of memristor-implemented RC system in a material view are envisaged.