A Machine Learning Framework for Quantum Cascade Laser Design
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909222174720000 |
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| author | Hernandez, Andres Correa Gmachl, Claire F. |
| author_facet | Hernandez, Andres Correa Gmachl, Claire F. |
| contents | A multi-layer perceptron neural network was used to predict the laser transition figure of merit, a measure of the laser threshold gain, of over 900 million Quantum Cascade Laser designs using only layer thicknesses and the applied electric field as inputs. Designs were generated by randomly altering the layer thicknesses of an initial 10-layer design. Validating the predictions with our 1D Schrödinger solver, the predicted values show 5% to 15% error for structures where a laser transition could occur, and 35% to 70% error for structures where there was no laser transition. The algorithm allowed (i) for the identification of high figure of merit structures, (ii) recognition of which layers should be altered to maximize the figure of merit at a given electric field, and (iii) increased the original design figure of merit of 94.7 to 141.2 eV ps Å^2, a 1.5-fold improvement and significant for QC lasers. The computational time for laser design data collection is greatly reduced from 32 hours for 27000 designs using our 1D Schrödinger solver on a virtual machine, to 8 hours for 907 million designs using the machine learning algorithm on a laptop computer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Machine Learning Framework for Quantum Cascade Laser Design Hernandez, Andres Correa Gmachl, Claire F. Optics A multi-layer perceptron neural network was used to predict the laser transition figure of merit, a measure of the laser threshold gain, of over 900 million Quantum Cascade Laser designs using only layer thicknesses and the applied electric field as inputs. Designs were generated by randomly altering the layer thicknesses of an initial 10-layer design. Validating the predictions with our 1D Schrödinger solver, the predicted values show 5% to 15% error for structures where a laser transition could occur, and 35% to 70% error for structures where there was no laser transition. The algorithm allowed (i) for the identification of high figure of merit structures, (ii) recognition of which layers should be altered to maximize the figure of merit at a given electric field, and (iii) increased the original design figure of merit of 94.7 to 141.2 eV ps Å^2, a 1.5-fold improvement and significant for QC lasers. The computational time for laser design data collection is greatly reduced from 32 hours for 27000 designs using our 1D Schrödinger solver on a virtual machine, to 8 hours for 907 million designs using the machine learning algorithm on a laptop computer. |
| title | A Machine Learning Framework for Quantum Cascade Laser Design |
| topic | Optics |
| url | https://arxiv.org/abs/2406.07755 |