High-performance neuromorphic computing based on ferroelectric synapses with excellent conductance linearity and symmetry

Shu-Ting Yang, Xingyu Li, Tongliang Yu, Jie Wang, Hong Fang, Fang Nie, Bing He, Le Zhao, Weimin Lu, Shishen Yan, Alain Nogaret, Gang Liu, Limei Zheng

Research output: Contribution to journalArticlepeer-review


Artificial synapses can boost neuromorphic computing to overcome the inherent limitations of von Neumann architecture. As a promising memristor candidate, ferroelectric tunnel junctions (FTJ) enable the authors to successfully emulate spike-timing-dependent synapses. However, the nonlinear and asymmetric synaptic weight update under repeated presynaptic stimulation hampers neuromorphic computing by favoring the runaway of synaptic weights during learning. Here, the authors demonstrate an FTJ whose conductivity varies linearly and symmetrically by judiciously combining ferroelectric domain switching and oxygen vacancy migration. The artificial neural network based on this FTJ-synapse achieves classification accuracy of 96.7% during supervised learning, which is the closest to the maximum theoretical value of 98% achieved to date. This artificial synapse also demonstrates stable unsupervised learning in a noisy environment for its well-balanced spike-timing-dependent plasticity response. The novel concept of controlling ionic migration in ferroelectric materials paves the way toward highly reliable and reproducible supervised and unsupervised learning strategies.

Original languageEnglish
Article number2202366
JournalAdvanced Functional Materials
Issue number35
Early online date22 Jun 2022
Publication statusPublished - 25 Aug 2022


  • electronic synapses
  • ferroelectric tunnel junctions
  • linear and symmetric weight changes
  • spike-timing-dependent plasticity

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics


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