Reinforcement learning approach for deterministic SOT-MRAM switching

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.