2D MoTe<sub>2</sub>/MoS<sub>2−x</sub>O<sub>x</sub> Van der Waals Heterostructure for Bimodal Neuromorphic Optoelectronic Computing
Yang Xiao, Wenbo Li, Xiankai Lin, Y. Y. Ji, Zhilong Chen, Yan‐Ping Jiang, Qiu‐Xiang Liu, Xingui Tang, Qijie Liang
Abstract
Abstract The von Neumann bottleneck has long been a significant obstacle to the advancement of the era of intelligent computing. Neuromorphic devices are considered a promising solution to overcome this bottleneck. These devices draw inspiration from the information processing and computing capabilities of neurons in the human brain. Nevertheless, biomimetic synaptic devices used in neural network computing encounter significant challenges, including high nonlinearity in regulation, limited abundance of state conductance, and restrictions in unidirectional plasticity. Here, a memristor synaptic device is reported that utilizes the ion migration properties of MoTe 2 /MoS 2−x O x heterojunction interface. This device demonstrates remarkable exceptional linearity, extensive dynamic regulation, and bidirectional independently controllable synaptic plasticity when subjected to bimodal regulation using electrical and optical signals. In addition, it shows significant paired‐pulse facilitation, empirical learning, and spike‐timing‐dependent properties. Furthermore, a deep learning framework is constructed to evaluate the reliability of devices in neuromorphic computation. The electronic synapses achieve high accuracy rates of 99.3% and 96.5% in recognizing digits and floral graphics, while photonic synapses achieve 95.3% and 91.5%. These findings emphasize the superior performance of photonic synapses in synaptic computation and provide a potential methodology for integrating multimodal neuromorphic hardware with artificial intelligence computing systems.