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基于垂直范德华异质结构的多刺激响应突触

Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures.

机构信息

School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.

Key Laboratory of MEMS of Ministry of Education, School of Electronics Science and Engineering, Southeast University, Nanjing 210096, China.

出版信息

ACS Appl Mater Interfaces. 2022 Aug 10;14(31):35917-35926. doi: 10.1021/acsami.2c08335. Epub 2022 Jul 26.

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/WSe 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 (10 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.

摘要

脑启发智能系统需要超越简单功能的各种神经形态设备。在人工突触设备中将仿生传感与权重更新能力融合是关键研究重点之一。本文报道了一种具有突触和光传感功能的多响应突触器件。该器件采用垂直堆叠的石墨烯/ h-BN / WSe 异质结构,包括超高迁移率读出层、重量控制层和双刺激响应层。独特的结构使突触器件具有出色的突触可塑性、短的响应时间(3 μs)和出色的光响应度(10 A/W)。为了演示在神经形态计算中的应用,基于无监督尖峰神经网络(SNN)模拟手写数字识别,精度达到 90.89%,可与最先进的结果相媲美。此外,还展示了多端神经形态器件模拟树突整合和光开关逻辑。与其他突触器件不同,该研究工作验证了突触器件中的多功能集成,支持在神经形态网络中融合传感和自学习。

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