CMTJ

Table of contents

• CMTJ
  • Table of contents
  • Short description
    • What can you simulate?
  • Web GUI
  • Quickstart
    • Installation :rocket:
    • Extra dependencies
  • Documentation and examples
  • Claude Code skill
  • Extensions
  • Citing
• Development
  • Acknowledgements
  • Contributions
  • Docker
  • Precommit
  • Documentation builds

Short description

The cmtj name may be misleading -- the MTJ (Magnetic Tunnel Junctions) are not the only structures that may be simulated. The library allows for macromagnetic simulation of various multilayer spintronic structures. The package uses C++ implementation of (s)LLGS (stochastic Landau-Lifschitz-Gilbert-Slonczewski) equation with various field contributions included for instance: anisotropy, interlayer exchange coupling, demagnetisation, dipole fields etc. It is also possible to connect devices in parallel or in series to have electrically coupled arrays.

What can you simulate?

Below is a brief list of examples (it's not exhaustive! Check the docs for more).

Magnetic devices:

• Magnetic Tunnel Junctions
  • Voltage-Driven Magnetic Tunnel Junctions
  • Spin-Torque Oscillators
  • VCMA sensors and devices
  • Magnetic Tunnel Junction Arrays
• SOT devices
  • Current-Driven SOT
• Advanced device coupling
• Reservoirs (dipole coupling)
• Electrically coupled MTJs
• Base equations
  • Landau-Lifshitz-Gilbert-Slonczewski equation
  • Stochastic Landau-Lifshitz-Gilbert-Slonczewski equation
  • Landau-Lifshitz-Gilbert-Bloch equation
• Domain wall motion

Experimental methods:

Some of the experimental methods available:

• PIMM
• Spin-Diode
• CIMS
• R(H), M(H)

Web GUI

Check out the streamlit hosted demo here. You can simulate:

• PIMM spectra and Spin-Diode spectra
• Try some optimization fitting
• Fit multi-domain or multi-level M(H) or R(H) loops in Domain mode

Let us know if you have any issues with the demo.

Quickstart

Installation :rocket:

The recommended way is to use the pip package manager and virtualenv (or conda). Installation is as easy as doing:

Recommendation Use the following flags when installing/compiling from start. They provide optimum performance, but even without them, cmtj is very fast.

export LDFLAGS="-O3"
export CXXFLAGS="-O3 -march=native -ffast-math"

1. With virtualenv (recommended):

$(bash) python -m venv .my-venv
$(bash) source .my-venv/bin/activate
$(.my-venv) python -m pip install cmtj

2. Straight from pip:

python3 -m pip install cmtj

3. Straight from source:

python3 -m pip install git+https://github.com/LemurPwned/cmtj.git

4. Clone the repository:

git clone --recurse-submodules https://github.com/LemurPwned/cmtj.git
python3 -m pip install .

if your git is older, you may need to use --recursive instead of --recurse-submodules.

Extra dependencies

The package requires (if utils subpackage is used):

- numpy
- scipy
- matplotlib

Documentation and examples

Documentation: https://lemurpwned.github.io/cmtj. There are many examples available, check out the examples section in the docs

Claude Code skill

This repo ships a Claude Code skill (skills/cmtj-simulation) that teaches it how to write correct CMTJ simulations (units, layer setup, drivers, solvers, reproducibility). Install it with the skills CLI:

npx skills add LemurPwned/cmtj --skill cmtj-simulation

Or, from a local checkout of this repo:

npx skills add . --skill cmtj-simulation

Add -g to install globally instead of just this project. See skills/cmtj-simulation/SKILL.md for what it covers.

Extensions

There's a GUI version available! If you wish to conduct a subset of simulations, mainly for experimental modelling, please see the PyMag project. It uses CMTJ as a backend for fast computation.

Citing

We would appreciate citing either of the listed work if you decide to use the project or using the cite button on the right hand side panel of the repository:

cmtj: Simulation package for analysis of multilayer spintronic devices

@article{mojsiejuk_cmtj_2023,
	title = {cmtj: Simulation package for analysis of multilayer spintronic devices},
	volume = {9},
	issn = {2057-3960},
	url = {https://www.nature.com/articles/s41524-023-01002-x},
	doi = {10.1038/s41524-023-01002-x},
	pages = {54},
	number = {1},
	journaltitle = {npj Comput Mater},
	author = {Mojsiejuk, Jakub and Ziętek, Sławomir and Grochot, Krzysztof and Skowroński, Witold and Stobiecki, Tomasz},
	date = {2023-04-06},
}

Development

Acknowledgements

Many thanks to professor Jack Sankey for his help with the development of thermal contributions, with inspiration from the macrospinmob project.

Contributions

All contributions are welcome, please leave an issue if you've encountered any trouble with setup or running the library.

Docker

In the docker directory there's a Dockerfile that can be used to build a docker image with the library installed. Dockerfile.app is used for streamlit development.

Precommit

There's a .pre-commit-config.yaml that does some basic python and cpp lints and checks. More static analysis to come in the future. This may be run with:

pre-commit run -v

or

pre-commit run -a (or --files core/* cmtj/*)

Documentation builds

Note For stub generation add __init__.py to the cmtj directory.

There are a couple of stages to building the documentation

1. Build Doxygen documentation

   doxygen Doxyfile
   

   This is mostly for the C++ documentation. Future changes may couple C++ and Python docs.
2. Build stubs The stubgen is pybind11-stubgen or mypy stubgen with the latter being preferred now. Before running the stubgen, make sure to install the package with:

   python3 -m pip install .
   

   avoid using -e flag as it may cause issues with the stubgen. Then to generate, for instance, Stack module stubs we can do:

   stubgen -m cmtj.stack -o target-stub-dir/
   

   or

   python3 -c "import mypy.stubgen; mypy.stubgen.main(['-p', 'cmtj.stack', '-o', 'target-stub-dir/'])"
   

   More info here: https://mypy.readthedocs.io/en/stable/stubgen.html.
3. Parse stubs to Markdown. This stage is done by running: python3 docs/docgen.py The deployment of the documentation is done via:

   mkdocs gh-deploy
   

   But first, worth a check:

   mkdocs serve