Phase field study on the performance of artificial synapse device based on the motion of domain wall in ferroelectric thin films

Abstract

Artificial neural networks have gained intensive attention in recent years because of their potential in effectively reducing energy consumption and improving computation performance. Ferroelectric materials are considered to be promising candidates for artificial synapses because of their multiple and nonvolatile polarization states under external stimuli. Despite artificial ferroelectric synapses with multilevel states, long retention and fast switching speed have been reported, and some key fundamental issues, e.g., the influence of domain wall configuration and evolution on the performance of synapse behaviors, also remain unclear. In this work, we study the performance of artificial synapses based on the motion of 180° ferroelectric domain walls of stripe domain and cylinder domain in ferroelectric thin films via a dynamical phase field model. The results demonstrate that artificial synapses based on the stripe domain exhibit high linearity and symmetry in weight update under a weak electric field, compared with the cylinder domain. Based on such artificial synapses, the accuracy of an artificial neural network for the Modified National Institute of Standards and Technology handwritten digit recognition is over 92%. This work provides a domain-wall-based strategy to improve the weight updating linearity and symmetry of artificial synapse devices and the recognition accuracy of artificial neural networks.