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Stochastic magnetic tunnel junctions (sMTJ) using low-barrier nanomagnetshave shown promise as fast, energy-efficient, and scalable building blocks forprobabilistic 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 biasindependence preventing read disturbance (b) uniform randomness in themagnetization angle between the free layers, and (c) fast fluctuations withoutrequiring external magnetic fields while being robust to magnetic fieldperturbations. Here, we propose a new design satisfying all of theserequirements, using double-free-layer sMTJs with synthetic antiferromagnets(SAF). We evaluate the proposed sMTJ design with experimentally benchmarkedspin-circuit models accounting for transport physics, coupled with thestochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We findthat the use of low-barrier SAF layers reduces dipolar coupling, achievinguncorrelated fluctuations at zero-magnetic field surviving up to diametersexceeding ($D\approx 100$ nm) if the nanomagnets can be made thin enough($\approx 1$-$2$ nm). The double-free-layer structure retains bias-independenceand the circular nature of the nanomagnets provides near-uniform randomnesswith fast fluctuations. Combining our full sMTJ model with advanced transistormodels, 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 guidethe experimental development of superior stochastic magnetic tunnel junctionsfor large-scale and energy-efficient probabilistic computation for problemsrelevant to machine learning and artificial intelligence.

Double-Free-Layer Stochastic Magnetic Tunnel Junctions with Synthetic Antiferromagnets