Double-free-layer stochastic magnetic tunnel junctions with synthetic antiferromagnets

Stochastic magnetic tunnel junctions (SMTJs) 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-bits) are still lacking. Ideally, the SMTJs should have (a) voltage bias independence, preventing read disturbance; (b) uniform randomness in the magnetization angle between the two magnetic layers; and (c) fast fluctuations without requiring external magnetic fields, while being robust to magnetic field perturbations. Here, we propose a design that satisfies all of these requirements, using double-free-layer SMTJs with synthetic antiferromagnets (SAFs). 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 D1000. 2em{0ex}nm if the nanomagnets can be made thin enough (1--20. 2em{0ex}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 to be about 3. 60. 2em{0ex}fJ, with fluctuation rates of about 3. 30. 2em{0ex}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.