  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 ($D\approx 100$ nm) if the nanomagnets can be made thin enough
($\approx 1$-$2$ 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 $\approx$ 3.6 fJ,
with fluctuation rates of $\approx$ 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.
