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In this Science Advances paper, we measure wafer-scale SOT-MRAM and model the measured device performance in co-designed neural network and probabilistic computing tasks. You can find the data from the Science Advances paper on github. There is also a link in the paper to the CrossSim simulation framework on Zenodo.

Source: S. Liu, V. Rogers, C.-Y. Hu, M.-Y. Song, X. Bao, and J. A. C. Incorvia. “Wafer-Scale SOT-MRAM for Analog Crossbar Array Applications”. Science Advances 12, 35 (2026). <DOI 10.1126/sciadv.aee6952>.

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The Literature Enhanced Ab Initio Discovery (LEAD) pipeline generates a materials science corpus and trains LLMs on the corpus to predict new materials for tunnel junction devices, combined with rapid validation through density function theory (DFT) transport. Please see more information in the following publication:

S. Islam, W. S. Rogers, C.-Y. Hu, M.-Y. Song, X. Bao, S. Sayed, and J. A. C. Incorvia. “LEAD: Literature Enhanced Ab initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions”. Advanced Materials e18241 (2025). <DOI 10.1002/adma.202518241>.

Click here for the link to the GitHub!

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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!

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Repository for development of spiking neural network simulations for domain wall-magnetic tunnel junction artificial neurons. For more information on the model and some example applications, see:

[1] S. Liu et al., “Graphene-based artificial dendrites for bio-inspired learning ins piking neuromorphic systems,” in Nano Letters, vol. 24, pp. 7211-7218, May 2024, doi: 10.1021/acs.nanolett.4c00739.

[2] C. Cui et al., “Spintronic artificial neurons showing integrate-and-fire behavior with reliable cycling operation,” in Nano Letters, vol. 25, pp. 361-367, Dec. 2024, doi: 10.1021/acs.nanolett.4c05063.

Click here for the link to the GitHub!

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QuantumTransport.jl is a Julia package designed for simulating quantum transport phenomena in nanoscale electronic devices. For more information on the model and some example applications, see:

[1]  V. Rogers, et. al., “Bulk axial Landau levels in the helically magnetized Weyl Hamiltonian and application to multistate memory devices,”, in Phys. Rev. B, vol. 109, no. 115159, March 2024. doi: https://doi.org/10.1103/PhysRevB.109.115159.

Click here for the link to the GitHub!