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The device performance of indium zinc oxide (IZO) thin-film transistors (TFTs) is optimized through process optimizations. By jointly adjusting the annealing condition, the channel thickness and the sputtering atmosphere, the roughness and oxygen vacancies (Vos) are precisely regulated. The optimized IZO TFTs can achieve the highest field effect mobility of ~71.8 cm2/Vs with a threshold voltage of ~-0.6 V. Reliability of IZO TFTs under positive/negative bias stress is also examined. The interface quality and the Vo are two key factors influencing the device performance and reliability, confirmed by X-ray photoelectron spectroscopy and atomic force microscopy analysis.

Abstract Cellulose paper has emerged as an ideal sensing element for wearable pressure sensors owing to its inherent flexibility, high porosity, and light weight. However, traditional paper‐based pressure sensors use metal‐based materials as electrodes, which significantly limits the unique advantages of paper, particularly in terms of degradability. In this study, a degradable pressure sensor is designed by combining the highly conductive poly(3,4‐ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) Xuan paper electrode with a low‐conductivity PEDOT:PSS tissue paper sensitive layer. Notably, Xuan paper, also called rice paper, has been a prominent substrate owing to its softness and good durability. By introducing a perforated structure in the sensitive layer, a novel sensing mechanism, conductivity conversion under pressure, is realized to improve the sensitivity. The obtained sensor exhibits a high sensitivity (13.9 kPa−1 at < 8.3 kPa, 151 kPa−1 at 8.3–20.8 kPa), ensuring that it can precisely monitor the full‐range human activities. Additionally, as the sensor does not rely on metal materials, it can degrade in water or fire without causing any negative environmental impacts. These findings establish a new approach to producing highly sensitive degradable sensors, which hold significant potential for application in green electronics, paper‐based sensing matrices, new prosthetics, and other fields.

With the advancements in Web of Things, Artificial Intelligence, and other emerging technologies, there is an increasing demand for artificial visual systems to perceive and learn about external environments. However, traditional sensing and computing systems are limited by the physical separation of sense, processing, and memory units that results in the challenges such as high energy consumption, large additional hardware costs, and long latency time. Integrating neuromorphic computing functions into the sensing unit is an effective way to overcome these challenges. Therefore, it is extremely important to design neuromorphic devices with sensing ability and the properties of low power consumption and high switching speed for exploring in-sensor computing devices and systems. In this review, we provide an elementary introduction to the structures and properties of two common optoelectronic materials, perovskites and transition metal dichalcogenides (TMDs). Subsequently, we discuss the fundamental concepts of neuromorphic devices, including device structures and working mechanisms. Furthermore, we summarize and extensively discuss the applications of perovskites and TMDs in in-sensor computing. Finally, we propose potential strategies to address challenges and offer a brief outlook on the application of optoelectronic materials in term of in-sensor computing.

Designing efficient bio-inspired vision systems remains a challenge. Here, the authors report a bio-inspired striate visual cortex with binocular and orientation selective receptive field based on self-powered memristor to enable machine vision with brisk edge and corner detection in the future applications.

Abstract The essential step for developing neuromorphic systems is to construct more biorealistic elementary devices with rich spatiotemporal dynamics to exhibit highly separable responses in dynamic environmental circumstances. Unlike transistor‐based devices and circuits with zeroth‐order complexity, memristors intrinsically express some simple biomimetic functions. However, with only two‐terminal structure, precise control of operation principles to ensure large dynamic space, improved linearity and symmetry, multimodal operation as well as high‐order complexity is hard to achieve in a traditional memristor owing to its limited degree of freedom. Therefore, multiterminal memristors including both concrete terminals and virtual terminals (light, pressure, gas, ferroelectric polarity, etc.) have been proposed to obtain precise modulation of memristive characteristics. This review focuses on the recent progress in multiterminal memristors and their neuromorphic applications. The operation principle, application of multiterminal memristor on neuromorphic computing in different scenarios, existing challenges and future prospects are discussed.

Building the brain-inspired neural network computing system based neuromorphic electronics is an effective approach to break the von Neumann bottleneck on the hardware level and realize the information processing with high efficiency and low energy consumption in this big data explosion age. Triboelectric nanogenerator (TENG) has two functions of sensing and energy conversion, which promote the application as sensor and/or power supply in self-powered neuromorphic electronics for data storage and biological synapse/neuron behaviors mimicking. This article highlights the relevant works of TENGs for memory devices, artificial synapses and artificial neurons, performs a systematic comparison, and puts forward the future research possibilities and challenges, with the hope of attracting more researchers into this field and promoting the development of TENG based neuromorphic electronics.

Porphyrin‐based metal–organic frameworks (PP‐MOFs) have some special features beyond ordinary MOFs, including superior optoelectronic characteristics, the ability to form 2D layered structure, and customizability, which prompt the increasing attention of PP‐MOFs in the field of neuromorphic electronics. The related application research is in the initial stage, and a timely summary and guidance are necessary. The PP‐MOFs fabrication should be shifted from powder synthesis in a chemistry laboratory to high‐quality film preparation under a clean environment to ensure device performance. This article highlights the PP‐MOFs film preparation methods and the application advances in neuromorphic electronics, performs comparative analysis in detail, and puts forward the challenges and future research directions, with the aim to attract the attention of experts in various areas (e.g., chemists, materials scientists, and engineers) and promote the application of PP‐MOFs in neuromorphic electronics.

Abstract Neuromorphic system based on artificial synaptic devices is considered as a potential candidate to realize the in‐memory computing and parallel processing of data for overcoming the von Neumann bottleneck. However, to fully imitate the complicated functions of the biological neural networks at the hardware level is still a challenging task. In this work, a multi‐terminal MoS2 synaptic transistor is developed, which not only simulates various biological synaptic behaviors, including paired pulse facilitation (PPF), excitatory/inhibitory post‐synaptic current (EPSC/IPSC), spike‐rate‐dependent plasticity (SRDP), and spike‐timing‐dependent plasticity (STDP), but also can independently mimic the parallel signal processing and transmissions in biological multipolar neurons. By combining the multi‐terminal MoS2 synaptic transistor with the micro‐structured polydimethylsiloxane (PDMS) pressure sensors, an intelligent tactile recognition system is built up, which can realize the spatiotemporal recognition of touch position. Furthermore, with sensor selection, the spatiotemporal modulation of synaptic plasticity and the human learning and forgetting behaviors to the knowledge with different difficulty degrees can be mimicked. This work provides a novel interconnection scheme for simulating signal transmission and processing among neurons, showing broad application prospects of the multi‐terminal MoS2 synaptic transistor in intelligent human–computer interaction and bionic neuromorphic perception systems.

Abstract Members of the 2D group VA semiconductors (phosphorene, arsenene, antimonene, and bismuthine) present a new class of 2D materials, which are recently gaining a lot of research interest. These materials possess layered morphology, tunable direct bandgap, high charge carrier mobility, high stability, unique in‐plane anisotropy, and negative Poisson's ratio. They prepare the ground for novel and multifunctional applications in electronics, optoelectronics, and batteries. The most recent analytical and empirical developments in the fundamental characteristics, fabrication techniques, and potential implementation of 2D group VA materials in this review, along with presenting insights and concerns for the field's future are analyzed.

Development of real-time sensing capability in artificial vision system requires an integration that allow sensing, computation, and storage, whilst remain energy efficient and compact. Here, the authors mimic the lobula giant movement detector to achieve this objective via light-mediated threshold switching memristor.

Abstract Nonvolatile memory is distinguished for the application in many electronic products due to its excellent charge storage ability. Nevertheless, as the device dimensions are scaled down, floating gate memory encounters various challenges: the increasing leakage current leading to a serious reliability issue and the decreasing of charge density. Hence, metal nanoparticle‐based floating gate memory has been proposed and become a promising candidate for nonvolatile memories due to its outstanding operation speed, excellent scalability, and favorable reliability. This review briefly introduces the classification of memory devices. The operation mechanisms, fabrication and characterization of metal nanoparticle‐based floating gate memory are discussed based on research activities reported in recent years.

Emerging intelligent devices that can simulate an artificial intelligence vision system are of great interest for the development of modern information technology. Nociceptor is a crucial sensory neuron that recognizes harmful inputs and sends pain signals to the central nervous system to avoid injury; however, visual nociceptors, considered to be a key bionic function to protect eyesight based on optoelectronic devices, have yet to be developed. Herein this study, a three‐terminal flexible memory phototransistor (MPT) is first fabricated, which simulates the visual nociceptive behavior by adjusting light stimulation. The CsPbBr3 quantum‐dots (QDs)‐few‐layered black phosphorous nanosheets (FLBP NSs) heterojunction MPT demonstrates high responsivity of 7.2 × 103 AW−1 and high detectivity of 1.8 × 1013 Jones due to the high absorption coefficient of CsPbBr3 QDs materials and a high carrier transport property of FLBP NSs. Moreover, the proposed device can be used to emulate ultraviolet‐stimuli‐induced characteristics of visual nociceptors such as a threshold, no adaption, relaxation, allodynia, and hyperalgesia. It provides a new avenue for the realization of next‐generation neural‐integrated devices via its visual pain sense‐perception abilities.

In recent years, halide perovskite quantum dots (HP-QDs) based composites have been widely developed and used in various applications owing to their unique photonic, electronic and mechanical properties, as well as high stability to water, oxygen, heat and illumination. Remarkable efforts have been made in the synthesis and applications of these materials in photonics, electronics, sensors and other fields. Besides these topics, we also cover enhancement of optoelectronic properties as well as chemical, thermal and photostability of HP-QDs-based composites. We hope this review will promote both the development and applications of perovskite-based materials.

Natural biomaterials are potential candidates for the next generation of green electronics due to their biocompatibility and biodegradability. On the other hand, the application of biocomposite systems in information storage, photoelectrochemical sensing, and biomedicine has further promoted the progress of environmentally benign bioelectronics. Here, we mainly review recent progress in the development of biocomposites in data storage, focusing on the application of biocomposites in resistive random-access memory (RRAM) and field effect transistors (FET) with their device structure, working mechanism, flexibility, transient characteristics. Specifically, we discuss the application of biocomposite-based non-volatile memories for simulating biological synapse. Finally, the application prospect and development potential of biocomposites are presented.

Neuromorphic computing systems can perform memory and computing tasks in parallel on artificial synaptic devices through simulating synaptic functions, which is promising for breaking the conventional von Neumann bottlenecks at hardware level. Artificial optoelectronic synapses enable the synergistic coupling between optical and electrical signals in synaptic modulation, which opens up an innovative path for effective neuromorphic systems. With the advantages of high mobility, optical transparency, ultrawideband tunability, and environmental stability, graphene has attracted tremendous interest for electronic and optoelectronic applications. Recent progress highlights the significance of implementing graphene into artificial synaptic devices. Herein, to better understand the potential of graphene-based synaptic devices, the fabrication technologies of graphene are first presented. Then, the roles of graphene in various synaptic devices are demonstrated. Furthermore, their typical optoelectronic applications in neuromorphic systems are reviewed. Finally, outlooks for development of synaptic devices based on graphene are proposed. This review will provide a comprehensive understanding of graphene fabrication technologies and graphene-based synaptic device for optoelectronic applications, also present an outlook for development of graphene-based synaptic device in future neuromorphic systems.

The traditional von Neumann structure computers cannot meet the demands of high-speed big data processing; therefore, neuromorphic computing has received a lot of interest in recent years. Brain-inspired neuromorphic computing has the advantages of low power consumption, high speed and high accuracy. In human brains, the data transmission and processing are realized through synapses. Artificial synaptic devices can be adopted to mimic the biological synaptic functionalities. Nanowire (NW) is an important building block for nanoelectronics and optoelectronics, and many efforts have been made to promote the application of NW-based synaptic devices for neuromorphic computing. Here, we will introduce the current progress of NW-based synaptic memristors and synaptic transistors. The applications of NW-based synaptic devices for neuromorphic computing will be discussed. The challenges faced by NW-based synaptic devices will be proposed. We hope this perspective will be beneficial for the application of NW-based synaptic devices in neuromorphic systems.

MXenes are a rapidly growing family of 2D materials. The composition, morphology, structure, surface chemistry, and structural configuration of MXenes directly affect their electrochemical performance. For example, when used as the source and drain electrodes in a transistor, MXenes provide increased chemically active interfaces, reduced ion diffusion length, and improved inplane carrier/charge transport kinetics, thus improving the storage performance of the transistor significantly. The work function and bandgap of MXenes can be controlled by tailoring their structure and surface functional groups. This makes MXenes as the first choice for several storage applications. Herein, the latest progress in the synthesis and properties of MXenes is summarized, and it is aimed to boost the motivation of researchers for developing novel MXenes and improving their applications. It emphasizes on the importance of MXenes in high‐density data storage applications, such as flash memories and memristors. The applications of MXenes in artificial synapses, neurons, and logic circuits are also discussed. A future roadmap is outlined for realizing the storage applications of MXenes.

By virtue of energy efficiency, high speed, and parallelism, brain‐inspired neuromorphic computing is a promising technology to overcome the von Neumann bottleneck and capable of processing massive sophisticated tasks in the background of big data. The abilities of perceiving and reacting to events in artificial neuromorphic systems allow us to build the communicative electronic–biological interfaces to get closer to electronic life. Protein materials offer great application potentials in such a system due to their sustainability, low cost, controllable hierarchical structure, intrinsic biocompatibility, and biodegradability. Herein, a timely review of the development of protein‐based memories for data storage and neuromorphic computing is provided. Proteins’ unique mechanical, electronic, optical properties, and their broad applications are discussed. Then, the progress of protein‐based two‐terminal memristor and three‐terminal transistor‐type memory is reviewed, and their applications for data storage, logic circuit, and neuromorphic computing are introduced. Finally, the major challenges and outlook toward the future developing directions of protein‐based computing systems are pointed out.

The introduction of the “Internet of Things” (IoTs) concept has spawned a series of research and development of wearable sensors. Flexible field‐effect transistors (FETs) are considered to be potential sensing devices due to the variety of material utilization and the self‐amplifying function on electrical signals. FETs have demonstrated the ability of detecting different kinds of external stimuli and continuous monitoring functionalities. Herein, the recent progress achieved by the academia in wearable sensors based on flexible FETs, including pressure, temperature, chemical, and biological analytes, which are vital for the manufacturing of smart wearable devices, is summarized. The sensing mechanism for different sensors is introduced and an in‐depth discussion is presented, including material engineering, problems at the current stage, and future challenges.

Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

Abstract Depending on the mobile species involved in the resistive switching process, redox random access memories and conductive bridge random access memories are widely studied with distinct switching mechanisms. Although the two resistance switching types have faithfully proved to be electrochemically linked in metal oxide‐based memristive devices, the corresponding photo‐induced transition has not yet been realized. Here, a photo‐induced transition through the integration of a graphene layer into a titanium oxide‐based memory device is demonstrated. Coupled with Raman mapping and an electron energy loss spectroscopy technique, the photo‐induced interaction at the heterostructure of graphene/titanium oxide are considered to dominate the transition process. Moreover, a negative differential resistance effect is observed by controlling the applied voltage, which can be credited to the saturation of trap centers (oxygen vacancies) and the increase of interfacial barrier at the graphene/titanium oxide heterojunction.

Abstract Employing flash memories that function as the analogue synapse cleft for implementation of neural systems is a popular approach. For practical application in synapse mimicking, ambipolar trapping of both holes and electrons enables the weight updated linearity, symmetric conductivity variation, and desired variation margin, which are consistent with biological synapse. Nevertheless, most of the flash memory exhibits unipolar charge trapping behavior. Here, the first polyoxometalates (POMs)‐modulated reduced graphene oxide (RGO)‐based flash memory with ambipolar charge trapping characteristics is reported. Keggin type [PW12O40]3− (PW) is selected to function as both catalyst for in situ photoreduction of the graphene oxide (GO) into RGO under UV illumination and electron trapping sites. The electrons accepting property of PW induces energy level realignment between the PW/RGO interface and the subsequent ambipolar charge trapping. Furthermore, the selected features of biological synaptic behavior, including short‐term plasticity and long‐term plasticity as well as paired pulse facilitation and depression, are mimicked. It is worth noting that almost symmetrical potentiation and depression synaptic behavior is obtained. This work provides new opportunities to realize reconfigurable characteristics of electronic devices via engineering of POMs, which is significantly meaningful to emerging artificial intelligence.