4.6 Article

Synaptic Plasticity and Filtering Emulated in Metal-Organic Frameworks Nanosheets Based Transistors

期刊

ADVANCED ELECTRONIC MATERIALS
卷 6, 期 1, 页码 -

出版社

WILEY
DOI: 10.1002/aelm.201900978

关键词

2D nanosheets; artificial synapses; composite materials; metal-organic frameworks; organic electronics

资金

  1. National Natural Science Foundation of China [61974093, 51902205]
  2. RGC of Hong Kong [T42-103/16N]
  3. Guangdong Province Special Support Plan for High-Level Talents [2017TQ04X082]
  4. Guangdong Provincial Department of Science and Technology [2018B030306028]
  5. Science and Technology Innovation Commission of Shenzhen [JCYJ20180507182042530, JCYJ20180507182000722, JCYJ20170818143618288, JCYJ20180305124214580]
  6. Shenzhen Peacock Technological Innovation Project [KQJSCX20170727100433270]
  7. Natural Science Foundation of SZU
  8. Photonics Center of Shenzhen University

向作者/读者索取更多资源

Two-dimensional (2D) metal-organic frameworks (MOFs) are widely used in a variety of mature applications, including catalysis, drug delivery, and sensors. Based on their highly accessible active sites, 2D MOFs are expected to be good charge trapping elements. Using 2D MOF, Zn-TCPP (TCPP: tetrakis(4-carboxyphenyl)porphyrin), as charge trapping materials by a simple solution process, a three-terminal synaptic device which can realize the learning functions and signal transmission simultaneously is firstly fabricated. The as-fabricated synaptic device exhibits ambipolar charge carrier trapping performance, large current(on)/current(off) ratio (>10(3)) and excellent endurance (500 cycle times). Moreover, the common biological synaptic behaviors, including postsynaptic current under different temperature, pulse duration time and pulse voltage, paired-pulse facilitation, paired-pulse depression, spiking rate dependent plasticity, dynamic filtering, transition from short-term potentiation to long-term potentiation, learning-forgetting-relearning process, are successfully simulated using our synaptic transistor. This research is highly relevant for broadening the application range of 2D MOFs and has important enlightenment for future neuromorphic computing.

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