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]

Renewable and clean "green" electronics based on paper substrates is an emerging field with intensifying research and commercial interests, as the technology combines the unique properties of flexibility, cost efficiency, recyclability, and renewability with the lightweight nature of paper. Because of its excellent optical transmittance and low surface roughness, nanopaper can host many types of electronics that are not possible on regular paper. However, there can be tremendous challenges with integrating devices on nanopaper due to its shape stability during processing. Here we demonstrate for the first time that flexible organic field-effect transistors (OFETs) with high transparency can be fabricated on tailored nanopapers. Useful electrical characteristics and an excellent mechanical flexibility were observed. It is believed that the large binding energy between polymer dielectric and cellulose nanopaper, and the effective stress release from the fibrous substrate promote these beneficial properties. Only a 10% decrease in mobility was observed when the nanopaper transistors were bent and folded. The nanopaper transistor also showed excellent optical transmittance up to 83.5%. The device configuration can transform many semiconductor materials for use in flexible green electronics.

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Synaptic transistors stimulated by light waves or photons may offer advantages to the devices, such as wide bandwidth, ultrafast signal transmission, and robustness. However, previously reported light-stimulated synaptic devices generally require special photoelectric properties from the semiconductors and sophisticated device's architectures. In this work, a simple and effective strategy for fabricating light-stimulated synaptic transistors is provided by utilizing interface charge trapping effect of organic field-effect transistors (OFETs). Significantly, our devices exhibited highly synapselike behaviors, such as excitatory postsynaptic current (EPSC) and pair-pulse facilitation (PPF), and presented memory and learning ability. The EPSC decay, PPF curves, and forgetting behavior can be well expressed by mathematical equations for synaptic devices, indicating that interfacial charge trapping effect of OFETs can be utilized as a reliable strategy to realize organic light-stimulated synapses. Therefore, this work provides a simple and effective strategy for fabricating light-stimulated synaptic transistors with both memory and learning ability, which enlightens a new direction for developing neuromorphic devices.

Implementation of artificial intelligent systems with light‐stimulated synaptic emulators may enhance computational speed by providing devices with high bandwidth, low power computation requirements, and low crosstalk. One of the key challenges is to develop light‐stimulated devices that can response to light signals in a neuron‐/synapse‐like fashion. A simple and effective solution process to fabricate light‐stimulated synaptic transistors (LSSTs) based on inorganic halide perovskite quantum dots (IHP QDs) and organic semiconductors (OSCs) is reported. Blending IHP QDs and OSCs not only improves the charge separation efficiency of the photoexcited charges, but also induces delayed decay of the photocurrent in the IHP QDs/OSCs hybrid film. The enhanced charge separation efficiency results in high photoresponsivity, while the induced delayed decay of the photocurrent is critical to achieving light‐stimulating devices with a memory effect, which are important for achieving high synaptic performance. The LSSTs can respond to light signals in a highly neuron‐/synapse‐like fashion. Both short‐term and long‐term synaptic behaviors have been realized, which may lay the foundation for the future implementation of artificial intelligent systems that are enabled by light signals. More significantly, LSSTs are fabricated by a facile solution process which can be easily applied to large‐scale samples.