Artificial Synapse with High Weight-Updating Performance Based on Charge-Trapping Mechanism

Artificial synapses, basic units of neuromorphic hardware, have been studied to emulate synaptic dynamics, which are beneficial for realizing high-quality neural networks. Currently, two-dimensional (2D) material heterojunction structures are widely used in the study of artificial synapses; however, their dynamic weight-updating characteristics are restricted owing to their high nonlinearity and low symmetricity. In this study, we treated h-BN with oxygen plasma to form a charge-trapping layer (CTL), and we prepared 2D ReS₂/CTL/h-BN heterojunction synapses. The device achieves a large memory window and excellent synaptic performance and simulates the adaptive behavior of the human eye through the synergistic modulation of the optoelectronic double pulse. The mechanism of the effect of trap states in CTL on the weight-updating performance was analyzed, and the device was further optimized. In the long-term potentiation/depression (LTP/D) weight-updating characteristic of the device, the nonlinearity was reduced to 0.63 and symmetricity reached 41.25, which is superior to most similar devices reported to date. Therefore, this research provides insights into improving the LTP/D weight-updating performance of synaptic devices.