Electrical Coupling of Perpendicular Superparamagnetic Tunnel Junctions for Probabilistic Computing.

Compact and energy-efficient computing systems may advantageously harness nanoscale sources of randomness, such as superparamagnetic tunnel junctions (SMTJs). The collective behavior resulting from the coupling between such SMTJs could be helpful in the hardware implementation of cognitive computing systems where randomness is a low-cost way to encode and explore available information states. Using a simple linear circuit, we mutually couple two such perpendicular SMTJs through the stochastic jumps of their binary resistive states. This approach led to the largest mutual SMTJ coupling strength reported in the literature at this stage. This first demonstration opens a promising path for implementing larger networks of coupled SMTJs that, using simple connectivity schemes, could emulate energy-based models such as Boltzmann and Ising machines or stochastic-based brain-inspired neural networks. In the case of SMTJs, thermal fluctuations at room temperature are the source of randomness that makes the magnetization switch randomly between two states, leading to random changes in the voltages across the two SMTJs. As a result of this voltage change, the magnetization switching probability of coupled SMTJs is, in turn, modified. Using this mechanism, we found a nearly 36 % cross-correlation between the states of the two coupled nanodevices. We use a generalized Néel-Brown model applied to individual SMTJs reproducing the positive (attractive) coupling strength of the coupled SMTJs with a four-state Markov model. Based on this model, we predict the external conditions (applied magnetic field, electrical current) and SMTJ features needed to obtain negative (repulsive) coupling strength.