Simulating biological synapses with electronic devices is a re-emerging field of research. It is widely recognized as the first step in hardware building brain-like computers and artificial intelligent systems. Thus far, different types of electronic devices have been proposed to mimic synaptic functions. Among them, transistor-based artificial synapses have the advantages of good stability, relatively controllable testing parameters, clear operation mechanism, and can be constructed from a variety of materials. In addition, they can perform concurrent learning, in which synaptic weight update can be performed without interrupting the signal transmission process. Synergistic control of one device can also be implemented in a transistor-based artificial synapse, which opens up the possibility of developing robust neuron networks with significantly fewer neural elements. These unique features of transistor-based artificial synapses make them more suitable for emulating synaptic functions than other types of devices. However, the development of transistor-based artificial synapses is still in its very early stages. Herein, this article presents a review of recent advances in transistor-based artificial synapses in order to give a guideline for future implementation of synaptic functions with transistors. The main challenges and research directions of transistor-based artificial synapses are also presented.In the nerve system, a synapse is a specialized structure that allows a neuron to pass chemical or electrical signals to another Figure 2. a) Schematic illustration (top) and microscopy image (bottom) of flexible synaptic transistors based on a random matrix of semiconducting CNTs. b) Case 1: the amplitudes of V LTP and V LTP are greater than other cases; thus, NL is the highest and ΔG is the largest. c) Case 2: the amplitudes of V LTP and V LTP are smaller than in case 1; thus, NL and ΔG are lower. d) Case 3: if the CNT transistor without the Au floating gate is used for the synaptic transistor, NL and ΔG are considerably smaller than in the other cases due to the limited charge storage space. Reproduced with permission. [87]

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the working principles of the brain, which may offer a new alternative paradigm for next-generation computation systems toward artificial intelligence. [3-8] In the nervous system, the transmission of information between neurons is usually performed in the chemical form of releasing neurotransmitters or in the electrical form of spikes, which is achieved through synapses. The ability of synapse to strengthen or weaken its connection strength between two neurons over time provides the physiological basis for synaptic computing and learning. [9,10] Therefore, the study of electronic devices with synaptic function is of great significance for constructing brainlike computing systems. [11-15] Thus far, lots of devices have been reported for simulating synaptic functions, including three/multi-terminal transistors [16-19] and two terminal resistant switching memories. [20-23] Compared with the two terminal devices, the transistorbased three/multi-terminal devices can write and read information synchronously. [24] Furthermore, external stimulus (e.g., light, pressure) can be easily converted into electrical signal in the transistor through proper material selection and device structure design. [25-28] So, it is possible to construct complex neuron networks with fewer transistor-based neuron elements. Previous studies on transistor-based artificial synapses mainly used traditional semiconductors as the functional layer to achieve synaptic functions. However, with the integrating development in neuromorphic chips, short channel effects will inevitably occur which strongly hinder the performance of devices, [29,30] such as the lowering in drain-induced-barrier and the increase in tunneling currents. [31,32] 2D materials with atomically layered scaling structure have only a limited vertical dimension and flat surfaces free from defects, [33,34] which have the potential to be immune to short channel effects. [35] It has been proved that it is feasible to fabricate sub-10 nm channel length devices with 2D semiconductors. [36] Thus, the emerging 2D semiconductor materials have attracted intensive research attention due to their unique size advantages, which may provide a feasible way for extending Moore's Law. [33] In addition, the 2D structure helps the active layer to gain good flexibility and optical transparency that the bulk semiconductors are sometimes hard to get. Ultrathin channels facilitate fast heat dissipation and quick respond to external stimuli, which is critical for the practical use of photosensitive electronic devices. [35,37] So far, reports on 2D active layer based 2D organic semiconductors (OSCs) with atomically layered scaling structure have been attracting intensive attention in recent years. Benefiting from their unique size advantages, 2D materials have the potential to be immune to short-channel effects. High-performance photoresponsive transistors based on 2D OSC films with excellent light-stimulated synaptic properties are reported. They exhibit a high I photo /I dark (up to 1.7 × 10 5), a competit...

Magnetic ZnFe2O4–C3N4 hybrids were successfully synthesized through a simple reflux treatment of ZnFe2O4 nanoparticles (NPs) (ca. 19.1 nm) with graphitic C3N4 sheets in methanol at 90 °C, and characterized by X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric and differential thermal analysis, X-ray photoelectron spectroscopy, high-resolution transmission electron microscopy, and UV–vis diffuse reflectance spectroscopy. Also, the catalytic activities of heterogeneous ZnFe2O4–C3N4 catalysts were evaluated in photo-Fenton discoloration toward Orange II using H2O2 as an oxidant under visible light (λ > 420 nm) irradiation. The reaction kinetics, degradation mechanism, and catalyst stability, as well as the roles of ZnFe2O4 and C3N4 in photoreaction, were comprehensively studied. It was found that the ZnFe2O4–C3N4 photocatalysts presented remarkable catalytic ability at neutral conditions, which is a great advantage over the traditional Fenton system (Fe2+/H2O2). The ZnFe2O4–C3N4 hybrid (mass ratio of ZnFe2O4/g-C3N4 = 2:3) exhibits the highest degradation rate of 0.012 min–1, which is nearly 2.4 times higher than that of the simple mixture of g-C3N4 and ZnFe2O4 NPs. g-C3N4 acted as not only a p-conjugated material for the heterojunction formation with ZnFe2O4, but also a catalyst for the decomposition of H2O2 to ·OH radicals. The heterogeneous ZnFe2O4–C3N4 hybrid exhibited stable performance without losing activity after five successive runs, showing a promising application for the photo-oxidative degradation of organic contaminants.

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Lead‐free perovskite materials are exhibiting bright application prospects in photodetectors (PDs) owing to their low toxicity compared with traditional lead perovskites. Unfortunately, their photoelectric performance is constrained by the relatively low charge conductivity and poor stability. In this work, photoresponsive transistors based on stable lead‐free bismuth perovskites CsBi3I10 and single‐walled carbon nanotubes (SWCNTs) are first reported. The SWCNTs significantly strengthen the dissociation and transportation of the photogenerated charge carriers, which lead to dramatically improved photoresponsivity, while a decent Ilight/Idark ratio over 102 can be maintained with gate modulation. The devices exhibit high photoresponsivity (6.0 × 104 A W−1), photodetectivity (2.46 × 1014 jones), and external quantum efficiency (1.66 × 105%), which are among the best reported results in lead‐free perovskite PDs. Furthermore, the excellent stability over many other lead‐free perovskite PDs is demonstrated over 500 h of testing. More interestingly, the device also shows the application potential as a light‐stimulated synapse and its synaptic behaviors are demonstrated. In summary, the lead‐free bismuth perovskite‐based hybrid phototransistors with multifunctional performance of photodetection and light‐stimulated synapse are first demonstrated in this work.

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