Ferroelectric Charged Domain-Wall Synapse for Neuromorphic Computing
Liang Chen, Yu Wang, Yiming Liu, Xiaoming Shi, Ji Ma, Wael Ben Taazayet, Qinghua Liang, Huayu Yang, Yuanyuan Fan, Jiafang Li, Congli He, Ying Fu, Houbing Huang, Jing Wang, Ce‐Wen Nan
Abstract
Inspired by brain neural networks, integrated memory-computing devices are critical to meet the demands of big data and artificial intelligence. This work explores the quasi-continuous modulation of ferroelectric charged domain walls' conductance, which is confined in a topological quad-domain, allowing the charged domain walls to serve as neural synapses. The device mimics synaptic plasticity (long-term potentiation and depression) and shows paired impulse facilitation. In a designed ferroelectric domain-wall neural network, we demonstrate multiplicative, accumulation-additive operations between the input image and the stored response matrix, capable of image processing functions, including triclassification with 100% accuracy. In the neural network simulation, the MINST database and the Cifar-10 database achieve 98.7% and 95.1% recognition rates. The sub-nanosecond polarization switching and the ultrathin (3-5 nm) charged domain walls position them as a promising platform for advancing ultrafast and scalable synaptic devices for low-power (potentially reduced to 0.2 aJ with sub-nanosecond pulse durations) neuromorphic computing systems.