Reinforcement learning approach for deterministic SOT-MRAM switching

Johannes Ender; Roberto L. de Orio; Simone Fiorentini; Siegfried Selberherr; Wolfgang Goes; Viktor Sverdlov

doi:10.1117/12.2593937

1 August 2021 Reinforcement learning approach for deterministic SOT-MRAM switching

Johannes Ender, Roberto L. de Orio, Simone Fiorentini, Siegfried Selberherr, Wolfgang Goes, Viktor Sverdlov

Proceedings Volume 11805, Spintronics XIV; 1180519 (2021) https://doi.org/10.1117/12.2593937
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We employ a reinforcement learning strategy for finding switching schemes for deterministic switching of a spin-orbit torque magnetoresistive random access memory cell. The free layer of the memory cell is perpendicularly magnetized, and the spin-orbit torques are generated by currents through two orthogonal heavy metal wires. A rewarding scheme for the reinforcement learning approach is defined such that the objective of the algorithm is to find a pulse sequence that leads to fast deterministic field-free switching of the memory cell. The reliability of the found switching scheme is tested by performing micromagnetic simulations. The results show that a neural network model trained on fixed material parameters is able to reverse the memory cell magnetization for a wide range of material parameters and can be used to derive a writing pulse sequence for fast and deterministic spin-orbit torque switching of a perpendicular free layer.

Conference Presentation

Citation Download Citation

Johannes Ender, Roberto L. de Orio, Simone Fiorentini, Siegfried Selberherr, Wolfgang Goes, and Viktor Sverdlov "Reinforcement learning approach for deterministic SOT-MRAM switching", Proc. SPIE 11805, Spintronics XIV, 1180519 (1 August 2021); https://doi.org/10.1117/12.2593937

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