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Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures.
Zhou, Jiachao; Li, Hanxi; Tian, Ming; Chen, Anzhe; Chen, Li; Pu, Dong; Hu, Jiayang; Cao, Jiehua; Li, Lingfei; Xu, Xinyi; Tian, Feng; Malik, Muhammad; Xu, Yang; Wan, Neng; Zhao, Yuda; Yu, Bin.
Afiliação
  • Zhou J; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Li H; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Tian M; Key Laboratory of MEMS of Ministry of Education, School of Electronics Science and Engineering, Southeast University, Nanjing 210096, China.
  • Chen A; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Chen L; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Pu D; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Hu J; Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining 314400, China.
  • Cao J; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Li L; School of Physical Science and Technology, Laboratory of Optoelectronic Materials and Detection Technology, Guangxi Key Laboratory for Relativistic Astrophysics, Guangxi University, Nanning 530004, China.
  • Xu X; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Tian F; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Malik M; Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining 314400, China.
  • Xu Y; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Wan N; Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining 314400, China.
  • Zhao Y; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Yu B; School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
ACS Appl Mater Interfaces ; 14(31): 35917-35926, 2022 Aug 10.
Article em En | MEDLINE | ID: mdl-35882423
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sinapses / Redes Neurais de Computação Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sinapses / Redes Neurais de Computação Idioma: En Ano de publicação: 2022 Tipo de documento: Article