Zhang Kun, Jia Xiaotao, Cao Kaihua, Wang Jinkai, Zhang Yue, Lin Kelian, Chen Lei, Feng Xueqiang, Zheng Zhenyi, Zhang Zhizhong, Zhang Youguang, Zhao Weisheng
Fert Beijing Research Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, P. R. China.
Beihang-Goertek Joint Microelectronics Institute, Qingdao Research Institute, Beihang University, Qingdao, 266101, P. R. China.
Adv Sci (Weinh). 2022 May;9(13):e2103357. doi: 10.1002/advs.202103357. Epub 2022 Feb 20.
Spintronic devices are considered as one of the most promising technologies for non-volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi-level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator. In this paper, a spintronic multi-level memory unit with high on/off ratio is proposed by integrating several series-connected magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) and a Schottky diode in parallel. Due to the rectification effect on the PMA MTJ, an on/off ratio over 100, two orders of magnitude higher than intrinsic values, is obtained under proper proportion of alternating current and direct current. Multiple resistance states are stably achieved and can be reconfigured by spin transfer torque effect. A computing-in-memory architecture based DNN accelerator for image classification with the experimental parameters of this proposal to evidence its application potential is also evaluated. This work can satisfy the rigorous requirements of DNN for memory unit and promote the development of high-accuracy and robust artificial intelligence applications.
自旋电子器件被认为是用于非易失性存储器和计算的最有前途的技术之一。然而，两个关键缺点，即缺乏固有的多级操作和低开/关比，极大地阻碍了它们在诸如深度神经网络（DNN）加速器等先进计算概念中的进一步应用。在本文中，通过将几个具有垂直磁各向异性（PMA）的串联连接磁隧道结（MTJ）与一个肖特基二极管并联集成，提出了一种具有高开/关比的自旋电子多级存储单元。由于对PMA MTJ的整流效应，在适当的交流和直流比例下，获得了超过100的开/关比，比固有值高两个数量级。稳定地实现了多个电阻状态，并且可以通过自旋转移矩效应进行重新配置。还评估了一种基于内存计算架构的DNN加速器，用于图像分类，并使用该提议的实验参数来证明其应用潜力。这项工作可以满足DNN对存储单元的严格要求，并促进高精度和强大的人工智能应用的发展。
