67 research outputs found
Double-Free-Layer Stochastic Magnetic Tunnel Junctions with Synthetic Antiferromagnets
Stochastic magnetic tunnel junctions (sMTJ) using low-barrier nanomagnets
have shown promise as fast, energy-efficient, and scalable building blocks for
probabilistic computing. Despite recent experimental and theoretical progress,
sMTJs exhibiting the ideal characteristics necessary for probabilistic bits
(p-bit) are still lacking. Ideally, the sMTJs should have (a) voltage bias
independence preventing read disturbance (b) uniform randomness in the
magnetization angle between the free layers, and (c) fast fluctuations without
requiring external magnetic fields while being robust to magnetic field
perturbations. Here, we propose a new design satisfying all of these
requirements, using double-free-layer sMTJs with synthetic antiferromagnets
(SAF). We evaluate the proposed sMTJ design with experimentally benchmarked
spin-circuit models accounting for transport physics, coupled with the
stochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We find
that the use of low-barrier SAF layers reduces dipolar coupling, achieving
uncorrelated fluctuations at zero-magnetic field surviving up to diameters
exceeding ( nm) if the nanomagnets can be made thin enough
(- nm). The double-free-layer structure retains bias-independence
and the circular nature of the nanomagnets provides near-uniform randomness
with fast fluctuations. Combining our full sMTJ model with advanced transistor
models, we estimate the energy to generate a random bit as 3.6 fJ,
with fluctuation rates of 3.3 GHz per p-bit. Our results will guide
the experimental development of superior stochastic magnetic tunnel junctions
for large-scale and energy-efficient probabilistic computation for problems
relevant to machine learning and artificial intelligence
Magnetic order in nanoscale gyroid networks
Three-dimensional magnetic metamaterials feature interesting phenomena that
arise from a delicate interplay of material properties, local anisotropy,
curvature, and connectivity. A particularly interesting magnetic lattice that
combines these aspects is that of nanoscale gyroids, with a
highly-interconnected chiral network with local three-connectivity reminiscent
of three-dimensional artificial spin ices. Here, we use finite-element
micromagnetic simulations to elucidate the anisotropic behaviour of nanoscale
nickel gyroid networks at applied fields and at remanence. We simplify the
description of the micromagnetic spin states with a macrospin model to explain
the anistropic global response, to quantify the extent of ice-like
correlations, and to discuss qualitative features of the anisotropic
magnetoresistance in the three-dimensional network. Our results demonstrate the
large variability of the magnetic order in extended gyroid networks, which
might enable future spintronic functionalities, including neuromorphic
computing and non-reciprocal transport.Comment: 10 pages, 6 figure
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