论文标题
用于内存计算的域壁挂式隧道连接旋转轨道扭矩设备和电路
Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices and Circuits for In-Memory Computing
论文作者
论文摘要
传统计算存在紧迫的问题,尤其是用于完成数据密集型和实时任务,这激发了内存计算设备的开发,以存储信息并执行计算。磁性隧道连接(MTJ)内存元件可通过操纵域壁(DW)(磁域之间的过渡区域)来用于计算。但是,这些设备遇到了挑战:DW的自旋传输扭矩(STT)切换需要高电流,并且在DW轨道顶部创建MTJ支柱所需的多个蚀刻步骤导致隧道磁路线固定(TMR)降低。这些问题对设备和电路的实验研究有限。在这里,我们研究了三端结构域壁 - 磁性隧道连接(DW-MTJ)内存计算设备的原型与使用自旋传递扭矩相比,电阻 - 区域产物RA =31Ω-μm^2,接近未公平膜的RA,开关电流密度较低。两个设备电路显示设备之间的位传播。通过控制DW初始位置,开关电压中的设备初始化变化被证明将其减少到7%,我们显示的对应于DW-MTJ完整加法器模拟中的96%精度。这些结果在使用MTJ和DWS进行内存和神经形态计算应用方面取得了长足的进步。
There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction (MTJ) memory elements can be used for computation by manipulating a domain wall (DW), a transition region between magnetic domains. But, these devices have suffered from challenges: spin transfer torque (STT) switching of a DW requires high current, and the multiple etch steps needed to create an MTJ pillar on top of a DW track has led to reduced tunnel magnetoresistance (TMR). These issues have limited experimental study of devices and circuits. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction (DW-MTJ) in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy (PMA), spin-orbit torque (SOT) switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 164%, resistance-area product RA = 31 Ω-μm^2, close to the RA of the unpatterned film, and lower switching current density compared to using spin transfer torque. A two-device circuit shows bit propagation between devices. Device initialization variation in switching voltage is shown to be curtailed to 7% by controlling the DW initial position, which we show corresponds to 96% accuracy in a DW-MTJ full adder simulation. These results make strides in using MTJs and DWs for in-memory and neuromorphic computing applications.