Luo Jie, Tian Guo, Zhang Ding-Guo, Zhang Xing-Chen, Lu Zhen-Ni, Zhang Zhong-Da, Cai Jia-Wei, Zhong Ya-Nan, Xu Jian-Long, Gao Xu, Wang Sui-Dong
Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China.
Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China.
ACS Appl Mater Interfaces. 2023 Oct 18;15(41):48452-48461. doi: 10.1021/acsami.3c09506. Epub 2023 Oct 6.
Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.
具有可调制极化程度的铁电材料有望用于探索电压驱动的神经形态硬件，其中直流电流可被最小化。利用绝缘铁电聚合物的单个有源层，我们开发了一种电压模式铁电突触，它可以连续且可逆地更新其状态。器件状态以可变输出电压的形式直接体现，使得多个铁电突触能够大规模直接级联以构建深度物理神经网络。这种基于电位叠加而非电流流动的神经网络类似于大脑中由动作电位驱动的生物神经网络。使用电压模式神经网络对手写数字识别进行模拟，准确率超过97%。压电力显微镜揭示的可控铁电极化结果表明，它负责铁电突触中的突触权重更新。本工作展示了一种用于新兴人工神经网络设计和构建的替代策略。
