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]

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.

Implementing synaptic functions with electronic devices is critical to achieve neuromorphic systems on the hardware platform, as synapses play important roles in brain computing and memory. Synapses modulated by light signals, which are also referred as photonic synapses, can not only make effective use of the outstanding properties of light to provide devices with ultrahigh propagation speed, high bandwidth and low crosstalk but also provide a noncontact writing method, which can facilitate the evolution of optical wireless communication and operation. More importantly, real‐time image processing can also be performed by photonic synapses which possess temporary memory. Thus far, tremendous efforts have been taken to design and fabricate photonic synapses. Herein, a summary of the development of different kinds of emerging materials utilized in photonic synaptic devices including memristors, field‐effect transistors, and phase change memory is presented, followed by the innovative applications of photonic synapses for neuromorphic systems. Finally, some current challenges and future study directions are discussed.

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...

The use of biocompatible and biodegradable materials in electronic devices can be an important trend in the development of the next-generation green electronics. In addition, by integrating the advantages of low power consumption, low-cost processing, and flexibility, organic synaptic devices will be promising elements for the construction of brain-inspired computers. However, previously reported electrolyte-gated synaptic transistors are mainly made of non-biocompatible and non-biodegradable electrolytes. Woods are widely considered as one kind of sustainable and renewable materials. We found that wood-derived cellulose nanopapers have ionic conductivity and, therefore, can be used as dielectric materials for organic synaptic transistors. The fabricated wood-derived cellulose nanopapers exhibit decent ionic conductivity of 7.3 × 10–4 S m–1 and a high lateral coupling effective capacitance of 18.65 nF cm–2 at 30 Hz. The laterally coupled organic synaptic transistors using wood-derived cellulose nanopapers as the dielectric layer present excellent transistor performances at the operating voltage below 1.5 V. More significantly, some important synaptic behaviors, such as excitatory postsynaptic current, signal-filtering characteristics, and dendritic integration are successfully simulated in our synaptic transistors. Because the development of electronic devices with biocompatible and biodegradable materials is essential, this work may inspire new directions for the development of “green” neuromorphic electronics.