4.8 Article

Multi-gate memristive synapses realized with the lateral heterostructure of 2D WSe2 and WO3

Journal

NANOSCALE
Volume 12, Issue 1, Pages 380-387

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/c9nr07941f

Keywords

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Funding

  1. National Key Research and Development Program of China [2018YFE0203802]
  2. National Natural Science Foundation of China [U1832116, 51772112]
  3. Fundamental Research Funds for the Central Universities [HUST: 2016YXZD058]
  4. HUAWEI Project [YBN2019055139]

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The development of novel synaptic device architectures with a high order of synaptic plasticity can provide a breakthrough toward neuromorphic computing. Herein, through the thermal oxidation of two-dimensional (2D) WSe2, unique memristive synapses based on the lateral heterostructure of 2D WSe2 and WO3, with multi-gate modulation characteristics, are firstly demonstrated. An intermediate transition layer in the heterostructure is observed through transmission electron microscopy. Raman spectroscopy and detailed electrical measurements provide insights into the mechanism of memristive behavior, revealing that the protons injected into/removed from the intermediate transition layer account for the memristive behavior. This novel memristive synapse can be used to emulate two neuron-based synaptic functions, like post-synaptic current, short-term plasticity and long-term plasticity, with remarkable linearity, symmetry, and an ultralow energy consumption of similar to 2.7 pJ per spike. More importantly, the synaptic plasticity between the drain and source electrodes can be effectively modulated by the gate voltage and visible light in a four-terminal configuration. Such multi-gate tuning of the synaptic plasticity cannot be accomplished by any previously reported multi-gate synaptic devices that only mimic two neuron-based synapses. This new synaptic architecture with electrical and optical modulation enables a realistic emulation of biological synapses whose synaptic plasticity can be additionally regulated by the surrounding astrocytes, greatly improving the recognition accuracy and processing capacity of artificial neuristors, and paving a new way for highly efficient neuromorphic computation devices.

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