Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing
Shengliang Cheng, Zhen Fan, Jingjing Rao, Lanqing Hong, Qicheng Huang, Ruiqiang Tao, Zhipeng Hou, Minghui Qin, Min Zeng, Xubing Lu, Guofu Zhou, Guoliang Yuan, Xingsen Gao, Jun‐Ming Liu
Abstract
FePV synapse is facilely grown on a silicon substrate, which demonstrates continuous photovoltaic response modulation with good controllability (small nonlinearity and write noise) enabled by gradual polarization switching. Using photovoltaic response as synaptic weight, this device exhibits versatile synaptic functions including long-term potentiation/depression and spike-timing-dependent plasticity. A simulated FePV synapse-based neural network achieves high accuracies (>93%) for image recognition. This study paves a new way toward highly controllable and silicon-compatible synapses for neuromorphic computing.