Reinforcement Learning Approach for Sub-Critical Current SOT-MRAM Switching

We present the use of reinforcement learning for the discovery of pulse sequences for optimal switching of spin-orbit torque magnetoresistive memory devices. A neural network trained on fixed material parameters is able to switch a memory cell for a wide range of material parameter variations as well as for sub-critical current values. Micromagnetic simulations are used to prove the reliability of the trained neural network.