Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures
Jiachao Zhou, Hanxi Li, Ming Tian, Anzhe Chen, Li Chen, Dong Pu, Jiayang Hu, Jiehua Cao, Lingfei Li, Xinyi Xu, Feng Tian, Muhammad Imran Malik, Yang Xu, Neng Wan, Yuda Zhao, Bin Yu
Abstract
Brain-inspired intelligent systems demand diverse neuromorphic devices beyond simple functionalities. Merging biomimetic sensing with weight-updating capabilities in artificial synaptic devices represents one of the key research focuses. Here, we report a multiresponsive synapse device that integrates synaptic and optical-sensing functions. The device adopts vertically stacked graphene/h-BN/WSe2 heterostructures, including an ultrahigh-mobility readout layer, a weight-control layer, and a dual-stimuli-responsive layer. The unique structure endows synapse devices with excellent synaptic plasticity, short response time (3 μs), and excellent optical responsivity (105 A/W). To demonstrate the application in neuromorphic computing, handwritten digit recognition was simulated based on an unsupervised spiking neural network (SNN) with a precision of 90.89%, well comparable with the state-of-the-art results. Furthermore, multiterminal neuromorphic devices are demonstrated to mimic dendritic integration and photoswitching logic. Different from other synaptic devices, the research work validates multifunctional integration in synaptic devices, supporting the potential fusion of sensing and self-learning in neuromorphic networks.