High-stable multifunctional dynamically reconfigurable artificial synapses based on hybrid graphene/ferroelectric field-effect transistors

作者：Liang Liu, Xutao Zhang, Ruijuan Tian, Qiao Zhang, Mingwen Zhang, Yu Zhang, Xuetao Gan · 发表于：Applied Physics Reviews · 年份：2025 · DOI：10.1063/5.0235614 · 被引用次数：7 · 研究领域：Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices、Graphene research and applications

In response to the challenges posed by traditional computing architectures in handling big data and AI demands, neuromorphic computing has emerged as a promising alternative inspired by the brain's efficiency. This study focuses on three-terminal synaptic transistors utilizing graphene and P(VDF-TrFE) to achieve dynamic reconfigurability between excitatory and inhibitory response modes, which are crucial for mimicking biological functions. The devices operate by applying different top gate spikes (±25 V and ±10 V) to modulate the polarization degree of P(VDF-TrFE), thereby regulating the carrier type and concentration in the graphene channel. This results in the effective realization of enhancement and inhibition processes in two neural-like states: excitatory and inhibitory modes, accompanied by good neural plasticity with paired-pulse facilitation and spike-time-dependent plasticity. With these features, the synaptic devices achieve brain-like memory enhancement and human-like perception functions, exhibiting excellent stability, durability over 1000 cycles, and a long retention period exceeding 10 years. Additionally, the performance of the artificial neural network is evaluated for handwritten digit recognition, achieving a high recognition accuracy of 92.28%. Our study showcases the development of highly stable, dynamically reconfigurable artificial synaptic transistors capable of emulating complex neural functions, providing a foundation for emerging neuromorphic comput...