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Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing.
Zhang, Qianyi; Hou, Binbin; Zhang, Jianya; Gu, Xiushuo; Huang, Yonglin; Pei, Renjun; Zhao, Yukun.
Afiliación
  • Zhang Q; College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, 210023, People's Republic of China.
  • Hou B; CAS Key Lab of Nanodevices and Applications, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), Suzhou, 215123, People's Republic of China.
  • Zhang J; CAS Key Laboratory for Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), Suzhou, 215123, People's Republic of China.
  • Gu X; School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei, 230026, People's Republic of China.
  • Huang Y; Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China.
  • Pei R; College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, 210023, People's Republic of China.
  • Zhao Y; CAS Key Lab of Nanodevices and Applications, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), Suzhou, 215123, People's Republic of China.
Nanotechnology ; 35(23)2024 Mar 18.
Article en En | MEDLINE | ID: mdl-38497449
ABSTRACT
Because of wide range of applications, the flexible artificial synapse is an indispensable part for next-generation neural morphology computing. In this work, we demonstrate a flexible synaptic device based on a lift-off (In,Ga)N thin film successfully. The synaptic device can mimic the learning, forgetting, and relearning functions of biological synapses at both flat and bent states. Furthermore, the synaptic device can simulate the transition from short-term memory to long-term memory successfully under different bending conditions. With the high flexibility, the excitatory post-synaptic current of the bent device only shows a slight decrease, leading to the high stability. Based on the experimental conductance for long-term potentiation and depression, the simulated three-layer neural network can achieve a high recognition rate up to 90.2%, indicating that the system comprising of flexible synaptic devices could have a strong learning-memory capability. Therefore, this work has a great potential for the development of wearable intelligence devices and flexible neuromorphic systems.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sinapsis / Dispositivos Electrónicos Vestibles Idioma: En Revista: Nanotechnology Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sinapsis / Dispositivos Electrónicos Vestibles Idioma: En Revista: Nanotechnology Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido