TY - GEN

T1 - Application of Machine Learning to Quantum Cascade Laser Design

AU - Hernandez, Andres Correa

AU - Gmachl, Claire F.

N1 - Funding Information:
ACKNOWLEDGMENTS Andres Correa Hernandez and Claire F Gmachl gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation. This work is supported by the National Science Foundation under Grant No. DGE-2039656. This research was partially funded by the Center for Statistics and Machine Learning at Princeton University through the support of Microsoft.
Publisher Copyright:
© 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - A framework to innovate quantum cascade laser design was developed using machine learning. An 8.2 μm laser with an operating field of 51 kV/cm and 131.7 eV ps Å2for the figure of merit was chosen as the starting design. A dataset of 13200 different designs was generated from the original, each design consisting of 22 well/barrier thicknesses with random well/barrier changes in the [-5, +5] Å range and an applied electric field from 20-70 kV/cm, for a total of 23 inputs. A second completely random dataset with 22 well/barrier thicknesses in the [9], [57] Å range was also developed, with 13200 designs and the same electric field sweep. The lasing transition figure of merit, gain coefficient, dipole matrix element, scattering times, and electronic state-pair energy difference were identified for each of these designs and were the outputs to be predicted. Single-output regression and multi-output regression were used to predict the figure of merit. Single-output regression was able to give root-mean-square error of 16 to 24 for the figure of merit in a matter of seconds. Multi-output regression predicts the root-mean-square error from 17 to 19 when trained for 10 to 30 minutes, sweeping through neural network parameters of interest. Both types of algorithms predicted data that was similar to training data very well but did not perform as well when new data was introduced.

AB - A framework to innovate quantum cascade laser design was developed using machine learning. An 8.2 μm laser with an operating field of 51 kV/cm and 131.7 eV ps Å2for the figure of merit was chosen as the starting design. A dataset of 13200 different designs was generated from the original, each design consisting of 22 well/barrier thicknesses with random well/barrier changes in the [-5, +5] Å range and an applied electric field from 20-70 kV/cm, for a total of 23 inputs. A second completely random dataset with 22 well/barrier thicknesses in the [9], [57] Å range was also developed, with 13200 designs and the same electric field sweep. The lasing transition figure of merit, gain coefficient, dipole matrix element, scattering times, and electronic state-pair energy difference were identified for each of these designs and were the outputs to be predicted. Single-output regression and multi-output regression were used to predict the figure of merit. Single-output regression was able to give root-mean-square error of 16 to 24 for the figure of merit in a matter of seconds. Multi-output regression predicts the root-mean-square error from 17 to 19 when trained for 10 to 30 minutes, sweeping through neural network parameters of interest. Both types of algorithms predicted data that was similar to training data very well but did not perform as well when new data was introduced.

KW - design

KW - figure of merit

KW - machine learning

KW - quantum cascade laser

KW - regression

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U2 - 10.1109/CISS56502.2023.10089756

DO - 10.1109/CISS56502.2023.10089756

M3 - Conference contribution

AN - SCOPUS:85154055776

T3 - 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023

BT - 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 57th Annual Conference on Information Sciences and Systems, CISS 2023

Y2 - 22 March 2023 through 24 March 2023

ER -