4,349 research outputs found
Ab initio studies of the spin-transfer torque in tunnel junctions
We calculate the spin-transfer torque in Fe/MgO/Fe tunnel junctions and
compare the results to those for all-metallic junctions. We show that the
spin-transfer torque is interfacial in the ferromagnetic layer to a greater
degree than in all-metallic junctions. This result originates in the half
metallic behavior of Fe for the states at the Brillouin zone center;
in contrast to all-metallic structures, dephasing does not play an important
role. We further show that it is possible to get a component of the torque that
is out of the plane of the magnetizations and that is linear in the bias.
However, observation of such a torque requires highly ideal samples. In samples
with typical interfacial roughness, the torque is similar to that in
all-metallic multilayers, although for different reasons.Comment: 5 pages, 4 figure
A numerical method to solve the Boltzmann equation for a spin valve
We present a numerical algorithm to solve the Boltzmann equation for the
electron distribution function in magnetic multilayer heterostructures with
non-collinear magnetizations. The solution is based on a scattering matrix
formalism for layers that are translationally invariant in plane so that
properties only vary perpendicular to the planes. Physical quantities like spin
density, spin current, and spin-transfer torque are calculated directly from
the distribution function. We illustrate our solution method with a systematic
study of the spin-transfer torque in a spin valve as a function of its
geometry. The results agree with a hybrid circuit theory developed by
Slonczewski for geometries typical of those measured experimentally.Comment: 13 pages, 8 figure
Identification of the dominant precession damping mechanism in Fe, Co, and Ni by first-principles calculations
The Landau-Lifshitz equation reliably describes magnetization dynamics using
a phenomenological treatment of damping. This paper presents first-principles
calculations of the damping parameters for Fe, Co, and Ni that quantitatively
agree with existing ferromagnetic resonance measurements. This agreement
establishes the dominant damping mechanism for these systems and takes a
significant step toward predicting and tailoring the damping constants of new
materials.Comment: 4 pages, 1 figur
A model describing the microwave emission from a multi-layer snowpack at 37 GHz
A multilayer emission model is described and applied to emission measurements obtained at 37 GHz and H polarization using a microwave radiometer attached to a truck-mounted boom in Steamboat Springs, Colorado in 1977. Estimated absorption and scattering coefficients and their dependence on wetness were obtained using calculated values of the dielectric constant at 37 GHz along with the model. It was found that the scattering coefficient is comparable in value to the absorption coefficient for dry snow however, the absorption coefficient increases linearly with increasing snow wetness while the scattering coefficient decreases linearly with increasing wetness. The emission from each layer of the snowpack was also calculated using the estimated coefficients. It is shown that for dry snow, the ground underneath the snowpack contributes about 45% of all measured emission while the rest is due to emission from all the layers within the snowpack. When the wetness of the top 5 cm layer of snowpack increases to about 2% by volume, this top 5 cm snowlayer contributes more than 90% of all the measured emission
Overcoming device unreliability with continuous learning in a population coding based computing system
The brain, which uses redundancy and continuous learning to overcome the
unreliability of its components, provides a promising path to building
computing systems that are robust to the unreliability of their constituent
nanodevices. In this work, we illustrate this path by a computing system based
on population coding with magnetic tunnel junctions that implement both neurons
and synaptic weights. We show that equipping such a system with continuous
learning enables it to recover from the loss of neurons and makes it possible
to use unreliable synaptic weights (i.e. low energy barrier magnetic memories).
There is a tradeoff between power consumption and precision because low energy
barrier memories consume less energy than high barrier ones. For a given
precision, there is an optimal number of neurons and an optimal energy barrier
for the weights that leads to minimum power consumption
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