Abstract
We demonstrate a bipolar graphene/F16CuPc synaptic transistor (GFST) with matched p-type and n-type bipolar properties, which emulates multiplexed neurotransmission of the release of two excitatory neurotransmitters in graphene and F16CuPc channels, separately. This process facilitates fast-switching plasticity by altering charge carriers in the separated channels. The complementary neural network for image recognition of Fashion-MNIST dataset was constructed using the matched relative amplitude and plasticity properties of the GFST dominated by holes or electrons to improve the weight regulation and recognition accuracy, achieving a pattern recognition accuracy of 83.23%. These results provide new insights to the construction of future neuromorphic systems.
Published Version
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