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.

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基于铁电薄膜畴壁运动的人工突触器件性能的相场研究

近年来，人工神经网络因其在有效降低能耗和提高计算性能方面的潜力而受到广泛关注。铁电材料被认为是人工突触的有希望的候选者，因为它们在外部刺激下具有多重和非易失性极化状态。尽管具有多级态的人工铁电突触、长保留和快速切换速度已被报道，但一些关键的基本问题，例如畴壁配置和进化对突触行为性能的影响，也仍不清楚。在这项工作中，我们通过动态相场模型研究了基于铁电薄膜中条纹域和圆柱域的 180°铁电畴壁运动的人工突触的性能。结果表明，与圆柱域相比，基于条纹域的人工突触在弱电场下的权重更新表现出较高的线性和对称性。基于这种人工突触，美国国家标准与技术研究院的人工神经网络手写数字识别准确率超过92%。这项工作提供了一种基于域墙的策略，以提高人工突触设备的权重更新线性和对称性以及人工神经网络的识别准确性。修改后的美国国家标准与技术研究院的人工神经网络手写数字识别准确率超过92%。这项工作提供了一种基于域墙的策略，以提高人工突触设备的权重更新线性和对称性以及人工神经网络的识别准确性。修改后的美国国家标准与技术研究院的人工神经网络手写数字识别准确率超过92%。这项工作提供了一种基于域墙的策略，以提高人工突触设备的权重更新线性和对称性以及人工神经网络的识别准确性。