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HighlightsBased on the benefits of two-dimensional (2D) transition metal chalcogenides (TMC) materials, the operating concepts and basics of memristors for neuromorphic computing are introduced.The prospects of 2D TMC materials and heterostructures are reviewed, as well as the state-of-the-art demonstration of 2D TMCs-based memristors for neuromorphic computing applications.The most recent advances, current challenges, and future prospects for the manufacture and characterization of memristive neuromorphic devices based on 2D TMCs are discussed.Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.

HighlightsA flexible tribotronic artificial synapse with bioinspired neurosensory behaviour was demonstrated, which establishes an active interaction mechanism with the environment.The device can well exhibit tuneable synaptic behaviours by changing the mechanical input modes, including excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memorial process.The device has excellent mechanical flexibility that can exhibit stable synaptic functions even under strain conditions with a bending radius of 20 mm after 1000 bending cycles.As key components of artificial afferent nervous systems, synaptic devices can mimic the physiological synaptic behaviors, which have attracted extensive attentions. Here, a flexible tribotronic artificial synapse (TAS) with bioinspired neurosensory behavior is developed. The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor, which can tune the carriers transport through the migration/accumulation of ions. The TAS successfully demonstrates a series of synaptic behaviors by external stimuli, such as excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memory process from sensory memory to short-term memory and long-term memory. Moreover, the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm, and the TAS still exhibits excellent durability after 1000 bending cycles. Finally, Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell, respectively. This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems, which is great significance to the practical application of artificial limbs, robotics, and bionics in future.

HighlightsThe latest progress of emerging smart flexible sensing systems driven by brain-inspired artificial intelligence (AI) from both the algorithm (machine learning) and the framework (artificial synapses) level is reviewed.New enabling features such as powerful data analysis and intelligent decision-making resulting from the fusion of AI technology with flexible sensors are discussed.Promising application prospects of AI-driven smart flexible sensing systems such as more intelligent monitoring for human activities, more humanoid feeling by artificial sensory organs, and more autonomous action of soft robotics are demonstrated.The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

HighlightsSynaptic devices that mimic synaptic functions are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.The working mechanisms, progress, and application scenarios of synaptic devices based on electrical and optical signals are compared and analyzed.The performances and future development of various synaptic devices that could be significant for building efficient neuromorphic systems are prospected.Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

HighlightsThe photoelectric response of violet phosphorus (VP) is significantly enhanced by the MXene/VP van der Waals heterojunctions and the highest photoresponsivity of VP is demonstrated.The first VP-based optoelectronic synapse with essential synaptic behaviours is demonstrated.Proof-of-concept visual-olfactory crossmodal perception based on MXene/VP optoelectronic synapses is explored to mimic neuromorphic vision with multi-sensory interactions.The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W−1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and “learning-experience” behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.

HfO2-based thin films possess broad application potential in semiconductors, non-volatile memory, and neuromorphic computing owing to their high dielectric constant, excellent ferroelectricity, and environmental robustness. However, an environmentally friendly strategy for synthesizing flexible HfO2-based films remains lacking. In this work, by incorporating a BaTiO3/Hf0.5Zr0.5O2/BaTiO3 (BTO/HZO/BTO) sandwiched architecture together with a Sr4Al2O7 sacrificial layer, a freestanding HfO2-based heterostructure can be detached simply by applying pure water. The resulting film exhibits high film quality and stable ferroelectric behavior, along with pronounced flexibility that enables repeated transfer onto diverse substrates. Furthermore, the synthesized freestanding film is employed to construct a ferroelectric field-effect transistor that successfully emulates synaptic functionalities, highlighting its potential for low-power neuromorphic hardware. This work provides a viable strategy for developing high-performance artificial synapses based on flexible oxide heterostructures.HfO2-based thin films possess broad application potential in semiconductors, non-volatile memory, and neuromorphic computing owing to their high dielectric constant, excellent ferroelectricity, and environmental robustness. However, an environmentally friendly strategy for synthesizing flexible HfO2-based films remains lacking. In this work, by incorporating a BaTiO3/Hf0.5Zr0.5O2/BaTiO3 (BTO/HZO/BTO) sandwiched architecture together with a Sr4Al2O7 sacrificial layer, a freestanding HfO2-based heterostructure can be detached simply by applying pure water. The resulting film exhibits high film quality and stable ferroelectric behavior, along with pronounced flexibility that enables repeated transfer onto diverse substrates. Furthermore, the synthesized freestanding film is employed to construct a ferroelectric field-effect transistor that successfully emulates synaptic functionalities, highlighting its potential for low-power neuromorphic hardware. This work provides a viable strategy for developing high-performance artificial synapses based on flexible oxide heterostructures.

Graphene oxide (GO)-based memristors offer the promise of low cost, eco-friendliness, and mechanical flexibility, making them attractive candidates for outstanding flexible electronic devices. However, their resistive transitions often display abrupt change rather than bidirectional progressive tuning, which largely limits their applications for biological synapse emulation and neuromorphic computing. Here, a memristor with a novel layered structure of GO/pyridinium/GO is presented with tunable bidirectional feature. The inserted organic pyridinium intercalation succeeds in serving as a satisfactory buffer layer to intrinsically control the formation of conductive filaments during device operation, leading to progressive conductance regulation. Thus, the essential synaptic behaviors including analog memory characteristics, excitatory postsynaptic current, paired pulse facilitation, prepulse inhibition, spike-timing-dependent plasticity, and spike-rate-dependent plasticity are replicated. The emulation of brainlike “learning-forgetting-relearning” process is also implemented. Additionally, the instant responses of the memristor can be stimulated by low operational voltages and short pulse widths. This study paves one way for GO-based memristors to actuate appealing features such as bidirectional tuning and fast speed switching that are desirable for the development of bio-inspired neuromorphic systems.

Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1. A biodegradable and flexible polymer‐based memristor for eco‐friendly artificial synapses is demonstrated. The developed device exhibits superior biodegradability and mechanical flexibility due to the high water solubility and excellent tensile strength of the polymer, respectively. Moreover, the memristors are operated as reliable synaptic cells with optimized synaptic plasticity, which is ideal for artificial neural networks with high energy efficiency.

Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.

The wearable tactile sensors have attracted great attention in the fields of intelligent robots, healthcare monitors and human-machine interactions. To create active tactile sensors that can directly generate electrical signals in response to stimuli from the surrounding environment is of great significance. Triboelectric nanogenerators (TENGs) have the advantages of high sensitivity, fast response speed and low cost that can convert any type of mechanical motion in the surrounding environment into electrical signals, which provides an effective strategy to design the self-powered active tactile sensors. Here, an overview of the development in TENGs as tactile stimulators for multifunctional sensing and artificial synapses is systematically introduced. Firstly, the applications of TENGs as tactile stimulators in pressure, temperature, proximity sensing, and object recognition are introduced in detail. Then, the research progress of TENGs as tactile stimulators for artificial synapses is emphatically introduced, which is mainly reflected in the electrolyte-gate synaptic transistors, optoelectronic synaptic transistors, floating-gate synaptic transistors, reduced graphene oxides-based artificial synapse, and integrated circuit-based artificial synapse and nervous systems. Finally, the challenges of TENGs as tactile stimulators for multifunctional sensing and artificial synapses in practical applications are summarized, and the future development prospects are expected.