Quantum cascade laser transition code

Correa Hernandez, Andres; Gmachl, Claire F
Issue date: 16 February 2024
Cite as:
Correa Hernandez, Andres & Gmachl, Claire F. (2024). Quantum cascade laser transition code [Data set]. Princeton University. https://doi.org/10.34770/z3r2-hg07
  author      = {Correa Hernandez, Andres and
                Gmachl, Claire F},
  title       = {{Quantum cascade laser transition code}},
  publisher   = {{Princeton University}},
  year        = 2024,
  url         = {https://doi.org/10.34770/z3r2-hg07}

A code to identify the laser transition for a quantum cascade laser design based on the figure of merit. Variables such as the number of layers, and layer thicknesses, as well the applied electric field, materials composition, number of period repetitions, and layer tolerance ranges to generate random designs are specified. A folder containing a .csv file with all electronic state-pair transitions collected, a .png file of the bandstructure and the laser transition chosen (in red), for all electric field iterations, and a summary .csv file of all these laser transitions for a structure at each electric field is generated by the code. To use, first install ErwinJr2 on your computer. Then locate the "ErwinJr2" folder and copy these 6 files into that directory, overwriting the previous five files (Material.py, QCLayers.py, QCPlotter.py, QuantumTab.py, rFittings.py). Lastly, run the "acej-qcl-layer_10-lwrandom-v23.py" script using Python. The "summary-fomstar-3lu-eVmiddle-19.csv" file is generated after running the laser transition code, with all of the data collected for one structure at many electric fields. Running the script various times will generate random structures with the same electric field range. Joining these "summary" .csv files makes a QCL dataset.

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The software ErwinJr2 is publicly available at: https://github.com/ErwinJr2/ErwinJr2 Details on using the laser transition code to build QCL datasets to be used in machine learning can be found in: A. C. Hernandez and C. F. Gmachl, “Application of Machine Learning to Quantum Cascade Laser Design,” in 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023, 2023. doi: 10.1109/CISS56502.2023.10089756. More details on building QCL datasets can be found: A. C. Hernandez, M. Lyu and C. F. Gmachl, "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning," 2022 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2022, pp. 1-2, doi: 10.1109/SUM53465.2022.9858281 An example of a QCL dataset is found: Andres Correa, H., Gmachl, C. F., & Lyu, M. (2023). QCL Dataset, 10 Layer Structure, Tolerance [-2, +3] A, Electric Field [0,10,150] kV/cm [Data set]. Princeton University. https://doi.org/10.34770/R7NR-EE50

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# Filename Description Filesize
1 README.txt 5.49 KB
2 Material.py Replace in "ErwinJr2" folder 19.1 KB
3 QCL-layer_10-4rep-rand--2A_3A-efield_0-10-150-v23.zip Generated from running QCLTransition-v23.py 3.33 MB
4 QCLTransition-v23.py Replace in "ErwinJr2" folder 12.6 KB
5 QCLayers.py Replace in "ErwinJr2" folder 67.2 KB
6 QCPlotter.py Replace in "ErwinJr2" folder 6.5 KB
7 QuantumTab.py Replace in "ErwinJr2" folder 16.5 KB
8 rFittings.py Replace in "ErwinJr2" folder 1.54 KB