All Two‐dimensional Integration‐Type Optoelectronic Synapse Mimicking Visual Attention Mechanism for Multi‐Target Recognition

Abstract The human visual attention mechanism enables them to rapidly perceive important information and objects in a complex external scene; this effectively solves the problems of data redundancy, low‐resolution images, and substantial computing resources. The process by which the attention system reconstructs the visual information can be considered as integrating internal attention signals with external visual details in the postsynaptic neuron. However, electronic devices that simulate visual attention modulation by incorporating device characteristics into neuromorphic vision systems (NVSs) to achieve visual attention behavior are rarely reported. Herein, a synapse device that integrates optical and electrical stimulation is designed using ReS 2 /hBN/monolayer graphene heterojunction to mimic attention regulation and integrate multiple neuron signals successfully. The synapse array can imitate perceptual learning of the human visual system (HVS) to realize visual preprocessing, such as image contrast improvement and weak signal enhancement at the sensory terminal, and overcome data redundancy. Moreover, by applying gate voltage pulses, electric‐tunable synaptic plasticity is successfully observed, attributed to the carrier trapping and de‐trapping mechanism in the floating layer. Attention stabilization, fluctuation, distraction, and reinforcement are exhibited, simulating the attention behaviors of the HVS. Thus, an NVS with attention mechanism is established depending on the optoelectronic hybrid synaptic plasticity of the device, which successfully mimics the HVS to perform a multi‐target recognition task. Furthermore, the effect of device defects on the NVS is rarely evaluated, in which a method is provided to analyze the application results of the NVS when considering uniformity and fault rate. This study may provide new inspiration for developing neuromorphic vision systems for autonomous driving and brainwave control in the future.