  Naturally random devices that exploit ambient thermal noise have recently
attracted attention as hardware primitives for accelerating probabilistic
computing applications. One such approach is to use a low barrier nanomagnet as
the free layer of a magnetic tunnel junction (MTJ) whose magnetic fluctuations
are converted to resistance fluctuations in the presence of a stable fixed
layer. Here, we propose and theoretically analyze a magnetic tunnel junction
with no fixed layers but two free layers that are circularly shaped disk
magnets. We use an experimentally benchmarked model that accounts for finite
temperature magnetization dynamics, bias-dependent charge and spin-polarized
currents as well as the dipolar coupling between the free layers. We obtain
analytical results for statistical averages of fluctuations that are in good
agreement with the numerical model. We find that the free layers with low
diameters fluctuate to randomize the resistance of the MTJ in an approximately
bias-independent manner. We show how such MTJs can be used to build a binary
stochastic neuron (or a p-bit) in hardware. Unlike earlier stochastic MTJs that
need to operate at a specific bias point to produce random fluctuations, the
proposed design can be random for a wide range of bias values, independent of
spin-transfer-torque pinning. Moreover, in the absence of a carefully optimized
stabled fixed layer, the symmetric double-free layer stack can be manufactured
using present day Magnetoresistive Random Access Memory (MRAM) technology by
minimal changes to the fabrication process. Such devices can be used as
hardware accelerators in energy-efficient computing schemes that require a
large throughput of tunably random bits.
