Reproducible Ultrathin Ferroelectric Domain Switching for High‐Performance Neuromorphic Computing

• Published in: Advanced materials (Weinheim) Vol. 32; no. 7; pp. e1905764 - n/a
• Main Authors: Li, Jiankun, Ge, Chen, Du, Jianyu, Wang, Can, Yang, Guozhen, Jin, Kuijuan
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.02.2020
• Subjects: Algorithms; Artificial neural networks; Computation; Computer simulation; Electronic devices; electronic synapses; ferroelectric domain switching; Ferroelectric domains; Ferroelectric materials; ferroelectric tunnel junctions; Ferroelectricity; Handwriting recognition; Linearity; Materials science; Memristors; Neural networks; Neuromorphic computing; Resistance; Switching; Synapses; Tunnel junctions; ultrathin films; ultrathin films; ferroelectric domain switching; ferroelectric tunnel junctions; electronic synapses; neuromorphic computing
• ISSN: 0935-9648; 1521-4095; 1521-4095
• DOI: 10.1002/adma.201905764

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high‐performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short‐ and long‐term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set. Ferroelectric tunnel junctions are utilized to design robust electronic synapses with high performance. Nonvolatile multilevel conductance states with excellent retention property are achieved through gradual ferroelectric domain switching. A simulated artificial neural network with supervised learning exhibits classification accuracy of 96.4% for the MNIST handwritten data set.