Zhang Xueying, Cai Wenlong, Wang Mengxing, Pan Biao, Cao Kaihua, Guo Maosen, Zhang Tianrui, Cheng Houyi, Li Shaoxin, Zhu Daoqian, Wang Lin, Shi Fazhan, Du Jiangfeng, Zhao Weisheng
Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing 100191 China.
Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute of Beihang University Qingdao 266000 China.
Adv Sci (Weinh). 2021 Mar 8;8(10):2004645. doi: 10.1002/advs.202004645. eCollection 2021 May.
Spin-torque memristors are proposed in 2009, and can provide fast, low-power, and infinite memristive behavior for neuromorphic computing and large-density non-volatile memory. However, the strict requirements of combining high magnetoresistance, stable domain wall pinning and current-induced switching in a single device pose difficulties in physical implementation. Here, a nanoscale spin-torque memristor based on a perpendicular-anisotropy magnetic tunnel junction with a CoFeB/W/CoFeB composite free layer structure is experimentally demonstrated. Its tunneling magnetoresistance is higher than 200%, and memristive behavior can be realized by spin-transfer torque switching. Memristive states are retained by strong domain wall pinning effects in the free layer. Experiments and simulations suggest that nanoscale vertical chiral spin textures can form around clusters of W atoms under the combined effect of opposite Dzyaloshinskii-Moriya interactions and the Ruderman-Kittel-Kasuya-Yosida interaction between the two CoFeB free layers. Energy fluctuation caused by these textures may be the main reason for the strong pinning effect. With the experimentally demonstrated memristive behavior and spike-timing-dependent plasticity, a spiking neural network to perform handwritten pattern recognition in an unsupervised manner is simulated. Due to advantages such as long endurance and high speed, the spin-torque memristors are competitive in the future applications for neuromorphic computing.
自旋扭矩忆阻器于2009年被提出,可为神经形态计算和大密度非易失性存储器提供快速、低功耗且具有无限忆阻行为的特性。然而,在单个器件中同时实现高磁阻、稳定的畴壁钉扎和电流诱导开关的严格要求给物理实现带来了困难。在此,通过实验展示了一种基于具有CoFeB/W/CoFeB复合自由层结构的垂直各向异性磁性隧道结的纳米级自旋扭矩忆阻器。其隧穿磁阻高于200%,并且可以通过自旋转移扭矩开关实现忆阻行为。忆阻状态通过自由层中的强畴壁钉扎效应得以保留。实验和模拟表明,在相反的Dzyaloshinskii-Moriya相互作用以及两个CoFeB自由层之间的Ruderman-Kittel-Kasuya-Yosida相互作用的共同作用下,W原子团簇周围可形成纳米级垂直手性自旋纹理。由这些纹理引起的能量波动可能是强钉扎效应的主要原因。基于实验证明的忆阻行为和脉冲时间依赖可塑性,模拟了一个以无监督方式执行手写模式识别的脉冲神经网络。由于具有诸如长耐久性和高速等优点,自旋扭矩忆阻器在神经形态计算的未来应用中具有竞争力。