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Complementary Graphene-Ferroelectric Transistors (C-GFTs) as Synapses with Modulatable Plasticity for Supervised Learning
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论文类型：论文集
发表刊物：2019 IEEE International Electron Devices Meeting
收录刊物：EI
学科门类：工学
一级学科：电子科学与技术
文献类型：C
DOI码：10.1109/IEDM19573.2019.8993453
发表时间：2019-12-06
摘要：Novel complementary graphene-ferroelectric transistors (C-GFTs) based synapses are proposed and experimentally demonstrated for the first time. By exploiting the unique zero-bandgap property of graphene, GFT based synapses can be dynamically reconfigured between potentiative and depressive (PD) modes corresponding to hole-and electron dominated transport in graphene channels, respectively. Both modes demonstrate excellent linearity, small (2%) cycle-to-cycle variation and > 32 levels when used as synapses. By configurating the PD modes into a pair of C-GFTs, the hardware architecture of spiking neural networks (SNNs) can be substantially innovated, where the complicated circuitry previously required for supervised learning is now completely removed. With C-GFTs, a synapse footprint of 100 μm 2 and a power consumption of 8 pJ/per operation are demonstrated in the MNIST learning task.