Multilevel Ferroelectric Domain Wall Memory for Neuromorphic Computing
Bo Shen, Haoran Sun, Xianyu Hu, Jie Sun, Jun Jiang, Z. Zhang, Anquan Jiang
Abstract
Abstract Low‐power and parallel processing Neuromorphic computing that imitates on the human brain's extraordinary data processing and learning capabilities is promising in non‐von Neumann application platforms in the post‐Moore era. Ferroelectric domain walls appearing as nanoscale topological defects in solids exhibit coexisting ordering parameters besides the spontaneous polarization, leading to the emergence of rich physics and electronic effects in the development of neuromorphic electronics that go beyond the conventional CMOS. In this study, the four‐state domain wall memory is developed on LiNbO 3 ferroelectric thin films integrated with Si substrates, where each state can be stably manipulated at a specific low voltage, showcasing outstanding fatigue resistance and retention performance. The constructed crossbar array that functions as a kernel for image convolution can be used to implement processing operations like image filtering, sharpness enhancement, and edge detection. Additional simulation from a convolutional neural network model shows 96.98% accuracy on the Modified National Institute of Standards and Technology handwritten digits dataset. The stable multi‐state domain wall memory provides a new approach for computing and promotes research in domain wall electronics to meet future enormous demands of big data.