Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing. Inspired by recent progress in optogenetics and visual sensing, light has been increasingly incorporated into synaptic devices. This paves the way to optoelectronic synaptic devices with a series of advantages such as wide bandwidth, negligible resistance–capacitance (RC) delay and power loss, and global regulation of multiple synaptic devices. Herein, the basic functionalities of synaptic devices are introduced. All kinds of optoelectronic synaptic devices are then discussed by categorizing them into optically stimulated synaptic devices, optically assisted synaptic devices, and synaptic devices with optical output. Existing practical scenarios for the application of optoelectronic synaptic devices are also presented. Finally, perspectives on the development of optoelectronic synaptic devices in the future are outlined.

Optoelectronic synaptic devices have been attracting increasing attention due to their critical role in the development of neuromorphic computing based on optoelectronic integration. Here we start with silicon nanomembrane (Si NM) to fabricate optoelectronic synaptic devices. Organolead halide perovskite (MAPbI 3 ) is exploited to form a hybrid structure with Si NM. We demonstrate that synaptic transistors based on the hybrid structure are very sensitive to optical stimulation with low energy consumption. Synaptic functionalities such as excitatory post-synaptic current (EPSC), paired-pulse facilitation, and transition from short-term memory to long-term memory (LTM) are all successfully mimicked by using these optically stimulated synaptic transistors. The backgate-enabled tunability of the EPSC of these devices further leads to the LTM-based mimicking of visual learning and memory processes under different mood states. This work contributes to the development of Si-based optoelectronic synaptic devices for neuromorphic computing.

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Optoelectronic synaptic devices that mimic biological synapses are critical building blocks of artificial neural networks (ANN) based on optoelectronic integration. Here it is shown that an optoelectronic synaptic device based on the hybrid structure of silicon nanocrystals (Si NCs) and poly(3-hexylthiophene) (P3HT) can work with dual modes, exhibiting versatile synaptic plasticity. In the three-terminal mode, the device is a synaptic transistor, which has wavelength-selective synaptic plasticity due to potential wells enabled by the Si NCs/P3HT hybrid structure. In the two-terminal mode, it is a synaptic metal-oxide-semiconductor (MOS) device, which is capable of mimicking spike-rate-dependent plasticity (SRDP) and metaplasticity with optical stimulation. Based on the wavelength-selective synaptic plasticity a light-stimulated ANN is proposed to recognize handwritten digits with an accuracy of around 90.4%. In addition, the SRDP and metaplasticity may be well used for the simulation of edge detection of images, facilitating real-time image processing.

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