Empirical Modeling of Superparamagnetic Magnetic Tunnel Junctions with Application to Probabilistic Computing

With the end of Moore's Law as we approach atomic scales, new and innovative ways are required to perform computation. Novel paradigms including beyond von Neumann architectures are one approach. An example is probabilistic computing, which leverages internal stochastic behavior for computation, one application being simulated annealing. To design next generation hardware architectures, we require high fidelity models capturing internal physics. In this work we describe an empirical model based on the Langevin equation that accurately captures quantitative metrics associated with one probabilistic bit realized in a superparamagnetic tunnel junction. We show how our model can be reduced to a one degree of freedom massless model capturing dynamics with high fidelity when compared to experimental data from a superparamagnetic tunnel junction. We then show how this one degree of freedom model can be used in computer design software enabling rapid prototyping of next generation computer architectures.