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Quantum imaging of the reconfigurable VO2 synaptic electronics for neuromorphic computing.
Feng, Ce; Li, Bo-Wen; Dong, Yang; Chen, Xiang-Dong; Zheng, Yu; Wang, Ze-Hao; Lin, Hao-Bin; Jiang, Wang; Zhang, Shao-Chun; Zou, Chong-Wen; Guo, Guang-Can; Sun, Fang-Wen.
Afiliação
  • Feng C; CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China.
  • Li BW; CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China.
  • Dong Y; National Synchrotron Radiation Laboratory, School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230029, China.
  • Chen XD; CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China.
  • Zheng Y; CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China.
  • Wang ZH; CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China.
  • Lin HB; CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China.
  • Jiang W; Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China.
  • Zhang SC; CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China.
  • Zou CW; CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China.
  • Guo GC; CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China.
  • Sun FW; CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China.
Sci Adv ; 9(40): eadg9376, 2023 Oct 06.
Article em En | MEDLINE | ID: mdl-37792938
ABSTRACT
Neuromorphic computing has shown remarkable capabilities in silicon-based artificial intelligence, which can be optimized by using Mott materials for functional synaptic connections. However, the research efforts focus on two-terminal artificial synapses and envisioned the networks controlled by silicon-based circuits, which is difficult to develop and integrate. Here, we propose a dynamic network with laser-controlled conducting filaments based on electric field-induced local insulator-metal transition of vanadium dioxide. Quantum sensing is used to realize conductivity-sensitive imaging of conducting filament. We find that the location of filament formation is manipulated by focused laser, which is applicable to simulate the dynamical synaptic connections between the neurons. The ability to process signals with both long-term and short-term potentiation is further demonstrated with ~60 times on/off ratio while switching the pathways. This study opens the door to the development of dynamic network structures depending on easily controlled conduction pathways, mimicking the biological nervous systems.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Adv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Adv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China