神经形态工程学
材料科学
铁电性
领域(数学分析)
非易失性存储器
磁畴壁(磁性)
纳米技术
工程物理
光电子学
人工智能
人工神经网络
计算机科学
电介质
物理
数学分析
数学
磁化
量子力学
磁场
作者
Bo Shen,Haoran Sun,Xianyu Hu,Jie Sun,Jun Jiang,Z. Zhang,Anquan Jiang
标识
DOI:10.1002/adfm.202315954
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI