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Modulation of Synaptic Plasticity Mimicked in Al Nanoparticle‐Embedded IGZO Synaptic Transistor
Author(s) -
Kim Jeehoon,
Kim Younghun,
Kwon Ojun,
Kim Taehyeon,
Oh Seyoung,
Jin Soeun,
Park Woojin,
Kwon JungDae,
Hong SeungWoo,
Lee ChangSik,
Ryu HoYong,
Hong Seoksu,
Kim Jaehoon,
Heo TaeYoung,
Cho Byungjin
Publication year - 2020
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201901072
Subject(s) - neuromorphic engineering , materials science , neural facilitation , synaptic plasticity , transistor , long term potentiation , nonsynaptic plasticity , excitatory postsynaptic potential , postsynaptic current , metaplasticity , optoelectronics , inhibitory postsynaptic potential , computer science , neuroscience , nanotechnology , artificial neural network , electrical engineering , voltage , artificial intelligence , chemistry , biology , engineering , biochemistry , receptor
Diverse artificial synapse structures and materials are widely proposed for neuromorphic hardware systems beyond von Neumann architecture owing to their capability to mimic complex information processing tasks such as image recognition, natural language processing, and learning. Nevertheless, temporal and spatial randomness in the movement of ion and electron particles that exist in materials usually prevents the solid‐state‐based synaptic devices from enabling the reliable modulation of synaptic plasticity. An aluminum nanoparticle (Al NP)‐embedded indium gallium zinc oxide (IGZO) synaptic transistor whose spike peak level and conductance change can be precisely modulated by the density of Al NPs within the IGZO channel is demonstrated. Essential synaptic functions including excitatory or inhibitory postsynaptic current, paired pulse facilitation, and short‐term potentiation or depression are also thoroughly emulated in the synaptic transistor device with the most optimized Al NP density: IGZO:Al NPs (6 nm). Moreover, controllable switching from short‐term to long‐term memory regimes essential for a learning task is demonstrated. Simulation results prove that this transistor can provide a decent recognition accuracy for neuromorphic computing. Indeed, the integrated IGZO:Al NP synaptic circuit with the effective synaptic plasticity will facilitate the implementation of a reconfigurable neuromorphic computing system.