In-plane dominant anisotropy stochastic magnetic tunnel junction for probabilistic computing: A Fokker–Planck study

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In-plane dominant anisotropy stochastic magnetic tunnel junction for probabilistic computing: A Fokker–Planck study
Title:
In-plane dominant anisotropy stochastic magnetic tunnel junction for probabilistic computing: A Fokker–Planck study
Journal Title:
Journal of Magnetism and Magnetic Materials
Publication Date:
01 September 2023
Citation:
Gan, C. K., Chen, B., & Zeng, M. (2023). In-plane dominant anisotropy stochastic magnetic tunnel junction for probabilistic computing: A Fokker–Planck study. Journal of Magnetism and Magnetic Materials, 586, 171197. https://doi.org/10.1016/j.jmmm.2023.171197
Abstract:
Recently there is considerable interest to realize efficient and low-cost true random number generators (RNGs) for practical applications. One important way is through the use of bistable magnetic tunnel junctions (MTJs). Here we study the magnetization dynamics of an MTJ, with a focus to realize efficient random bit generation under the assumption that the orientation dependence of the energy of the nanomagnet is described by two perpendicular in-plane anisotropies. We find that a high rate of random bit generation is achievable away from the pure easy-axis situation by tuning a single parameter so that it is either (a) toward a barrierless-like single easy plane situation when reduces to zero, or (b) toward a stronger easy plane situation when becomes increasingly negative where transitions between low energy states are confined in the stronger easy plane that contains the saddle points. We find that the MTJs maintain their fast magnetization dynamical characteristics even in the presence of a magnetic field. Our findings provide a valuable guide to achieving efficient generation of probabilistic bits for applications in probabilistic computing.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR - Career Development Fund
Grant Reference no. : C210812054
Description:
ISSN:
0304-8853
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