The Stochastic_MTJ_Model repository provides code for modelling Magnetic Tunnel Junction (MJT) devices operating with Spin-Orbit Torque, Spin-Transfer Torque, and/or Voltage Controlled Magnetic Anisotropy. The Landau-Lifshitz-Gilbert (LLG) Equation is solved in spherical coordinates in time using a macrospin approximation. The numerical solve of the LLG is implemented in Fortran callable by a provided Python interface. Our work with this model has been focused on modeling MTJ devices used for true random number generation. For more information on the model and some example applications, see:

[1] S. Liu et al., “Random Bitstream Generation Using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions,” in IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 8, no. 2, pp. 194-202, Dec. 2022, doi: 10.1109/JXCDC.2022.3231550.

[2] A. Maicke et al., “Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque,” in Nanotechnology, vol. 35, no. 275204, 2024, doi: 10.1088/1361-6528/ad3b01.

[3] S. G. Cardwell et al., “Device Codesign using Reinforcement Learning,” 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, Singapore, 2024, pp. 1-5, doi: 10.1109/ISCAS58744.2024.10558165.

Click here for the link to the GitHub!