Thermodynamic properties and switching dynamics of perpendicular shape anisotropy MRAM

Abstract

The power consumption of modern random access memory (RAM) has been a motivation for the development of low-power non-volatile magnetic RAM (MRAM). Based on a CoFeB/MgOmagnetic tunnel junction, MRAM must satisfy high thermal stability and a low writing current while being scaled down to a sub-20 nm size which is required to compete with the densities of current RAM technology. A recent development has been to exploit perpendicular shape anisotropy (PSA) along the easy axis by creating tower structures, with the free layers’ thickness (along the easy axis) being larger than its width. Here we use an atomistic model to explore the temperature dependent properties of thin cylindrical MRAM towers of 5 nm diameter while scaling down the free layer from 48-8 nm thick. We find thermal fluctuations are a significant driving force for the switching mechanism at operational temperatures by analysing the switching field distribution from hysteresis data. We find that a reduction of the free layer thickness below 18 nm rapidly loses shape anisotropy, and consequently stability, even at 0 K. Additionally, there is a change in switching mechanism for the 8 nm tower with coherent rotation being observed compared to all taller towers, which demonstrate incoherent rotation via a propagated domain wall.

Single current control of magnetization in vertical high-aspect-ratio nanopillars on in-plane magnetization layers

“…memory with high thermal stability and nonvolatility [1][2][3][4][5][6][7][8]. The magnetization of a pillar with a length-to-diameter aspect ratio greater than 1.0 is stabilized in the vertical direction relative to the substrate, which is in the longitudinal direction of the pillar, by either perpendicular shape anisotropy (PSA), perpendicular magnetic anisotropy, or both [9][10][11][12][13][14][15]. Furthermore, the development of multibit magnetic memories using pillars is also anticipated as high-density nonvolatile memories and as a perceptron for neuromorphic computing devices [16][17][18][19].…”