We demonstrate a bipolar graphene/F_(16)CuPc synaptic transistor(GFST)with matched p-type and n-type bipolar properties,which emulates multiplexed neurotransmission of the release of two excitatory neurotransmitters i...We demonstrate a bipolar graphene/F_(16)CuPc 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 F_(16)CuPc 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.展开更多
基金supported by the Shenzhen Science and Technology Program(No.JCYJ20210324121002008)the National Science Fund for Distinguished Young Scholars of China(No.T2125005)+5 种基金the National Key R&D Program of China(Nos.2022YFE0198200,2022YFA1204500,and 2022YFA1204504)the Natural Science Foundation of Tianjin(Nos.22JCYBJC01290 and 23JCQNJC01440)the Key Project of Natural Science Foundation of Tianjin(No.22JCZDJC00120)the Fundamental Research Funds for the Central Universities,Nankai University(Nos.BEG124901 and BEG124401)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515110319)the Key Science and Technology Program of Henan Province(No.242102210171).
文摘We demonstrate a bipolar graphene/F_(16)CuPc 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 F_(16)CuPc 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.
