Journal
ADVANCED FUNCTIONAL MATERIALS
Volume 28, Issue 42, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adfm.201804170
Keywords
electrochemical transistor; ion intercalation; molybdenum oxide; synaptic plasticity; synaptic transistor
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Funding
- National Nature Science Foundation of China [61874138, 51671213, 11534015, 51725104]
- National Key Research Program of China [2016YFA0300701]
- Chinese Academy of Sciences [XDB07030200]
- State Key Laboratory of Materials Processing and Die & Mould Technology [P2018-004]
- Nanostructures for Electrical Energy Storage (NEES), an Energy Frontier Research Center (EFRC) - U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DESC0001160]
- U.S. Department of Energy's National Nuclear Security Administration [DE-NA-0003525]
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Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two-terminal memristive devices are demonstrated to be possible candidates, they suffer from several shortcomings related to the filament formation mechanism including nonlinear switching, write noise, and high device conductance, all of which limit the accuracy and energy efficiency. Electrochemical three-terminal transistors, in which the channel conductance can be tuned without filament formation provide an alternative platform for synaptic electronics. Here, an all-solid-state electrochemical transistor made with Li ion-based solid dielectric and 2D alpha-phase molybdenum oxide (alpha-MoO3) nanosheets as the channel is demonstrated. These devices achieve nonvolatile conductance modulation in an ultralow conductance regime (<75 nS) by reversible intercalation of Li ions into the alpha-MoO3 lattice. Based on this operating mechanism, the essential functionalities of synapses, such as short- and long-term synaptic plasticity and bidirectional near-linear analog weight update are demonstrated. Simulations using the handwritten digit data sets demonstrate high recognition accuracy (94.1%) of the synaptic transistor arrays. These results provide an insight into the application of 2D oxides for large-scale, energy-efficient neuromorphic computing networks.
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