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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01bk128f226
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dc.contributor.authorCorrea Hernandez, Andres-
dc.contributor.authorGmachl, Claire F.-
dc.date.accessioned2024-03-20T15:44:25Z-
dc.date.available2024-03-20T15:44:25Z-
dc.date.created2023-07-24-
dc.date.issued2024-03-20-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01bk128f226-
dc.identifier.uriHttps://doi.org/10.34770/d644-0c85-
dc.description.abstractA dataset of 2400 quantum cascade structures at 15 electric field iterations, for a total of 36000 unique designs. The structures are generated by randomly altering a starting 10-layer design of alternating Al0.48In0.52As barrier material and In0.53Ga0.47As well material, with layer thickness sequence of 9/57/11/54/12/45/25/34/14/33 Angstroms (starting with well material). The random tolerance range is from -5 to +20 Angstroms in 5 Angstrom increments. The laser transition Figure of Merit, among other quantities of interest, is identified for each design using a method found in: 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/SUM53465.2022.9858281en_US
dc.description.tableofcontentsQCL-layer_10-4rep-rand-m5d5p20A-efield_10-10-150-v22-dataset.csven_US
dc.language.isoen_USen_US
dc.publisherPrinceton Universityen_US
dc.relationhttps://doi.org/10.34770/bps9-7152-
dc.relation.isreferencedbydoi: 10.1109/SUM53465.2022.9858281en_US
dc.rightsCC-BY 4.0en_US
dc.subjectQuantum Cascade Laser, Mid-Infrared, Machine Learning, Figure of Meriten_US
dc.titleQCL Dataset, 10 Layer Structure, Tolerance [-5, +20] Å, Electric Field [10,10,150] kV/cmen_US
dc.typeDataseten_US
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