Open AccessHigh On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network
Author(s) -
Zhang Kun,
Jia Xiaotao,
Cao Kaihua,
Wang Jinkai,
Zhang Yue,
Lin Kelian,
Chen Lei,
Feng Xueqiang,
Zheng Zhenyi,
Zhang Zhizhong,
Zhang Youguang,
Zhao Weisheng
Publication year - 2022
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202103357
Subject(s) - spintronics , rectification , computer science , spin transfer torque , artificial neural network , schottky diode , diode , materials science , electronic engineering , optoelectronics , artificial intelligence , electrical engineering , ferromagnetism , physics , magnetic field , engineering , condensed matter physics , magnetization , quantum mechanics , voltage
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.