Generating Quantum Cascade Laser Datasets for Applications in Machine Learning

Andres Correa Hernandez; Ming Lyu; Claire F. Gmachl

doi:10.1109/SUM53465.2022.9858281

Generating Quantum Cascade Laser Datasets for Applications in Machine Learning

Andres Correa Hernandez, Ming Lyu, Claire F. Gmachl

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

For the goal of training machine learning algorithms to predict quantum cascade laser designs with high optical gain, a model dataset containing 2600 designs was collected, filtered, and prepared using a representative 8.2 μm laser.

Original language	English (US)
Title of host publication	2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665434898
DOIs	https://doi.org/10.1109/SUM53465.2022.9858281
State	Published - 2022
Event	2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Cabo San Lucas, Mexico Duration: Jul 11 2022 → Jul 13 2022

Publication series

Name	2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings

Conference

Conference	2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022
Country/Territory	Mexico
City	Cabo San Lucas
Period	7/11/22 → 7/13/22

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Electrical and Electronic Engineering
Atomic and Molecular Physics, and Optics

Keywords

Machine Learning
Quantum Cascade Laser

Access to Document

10.1109/SUM53465.2022.9858281

Cite this

Hernandez, A. C., Lyu, M., & Gmachl, C. F. (2022). Generating Quantum Cascade Laser Datasets for Applications in Machine Learning. In 2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings (2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SUM53465.2022.9858281

Hernandez, Andres Correa ; Lyu, Ming ; Gmachl, Claire F. / Generating Quantum Cascade Laser Datasets for Applications in Machine Learning. 2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings).

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title = "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning",

abstract = "For the goal of training machine learning algorithms to predict quantum cascade laser designs with high optical gain, a model dataset containing 2600 designs was collected, filtered, and prepared using a representative 8.2 μm laser.",

keywords = "Machine Learning, Quantum Cascade Laser",

author = "Hernandez, {Andres Correa} and Ming Lyu and Gmachl, {Claire F.}",

note = "Funding Information: We gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation. This material is based upon work supported by the National Science Foundation under Grant No. DGE-2039656 Fig. 1. Conduction band diagram (blue) of the 8.2 μm QCL with three repetitions of the active region and injector, the electric field is 51 kV/cm; the squared modulus of the eigenfunctions for continuum eigenstates (grey), the bounded eigenstates (black), and three unique eigenstate pairs with the highest FoM (magenta, green, and red) using ErwinJr2. The green pair of eigenstates is chosen as the designated lasing transition. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 ; Conference date: 11-07-2022 Through 13-07-2022",

year = "2022",

doi = "10.1109/SUM53465.2022.9858281",

language = "English (US)",

series = "2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings",

address = "United States",

}

Hernandez, AC, Lyu, M & Gmachl, CF 2022, Generating Quantum Cascade Laser Datasets for Applications in Machine Learning. in 2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings. 2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022, Cabo San Lucas, Mexico, 7/11/22. https://doi.org/10.1109/SUM53465.2022.9858281

Generating Quantum Cascade Laser Datasets for Applications in Machine Learning. / Hernandez, Andres Correa; Lyu, Ming; Gmachl, Claire F.

2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Generating Quantum Cascade Laser Datasets for Applications in Machine Learning

AU - Hernandez, Andres Correa

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N1 - Funding Information: We gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation. This material is based upon work supported by the National Science Foundation under Grant No. DGE-2039656 Fig. 1. Conduction band diagram (blue) of the 8.2 μm QCL with three repetitions of the active region and injector, the electric field is 51 kV/cm; the squared modulus of the eigenfunctions for continuum eigenstates (grey), the bounded eigenstates (black), and three unique eigenstate pairs with the highest FoM (magenta, green, and red) using ErwinJr2. The green pair of eigenstates is chosen as the designated lasing transition. Publisher Copyright: © 2022 IEEE.

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N2 - For the goal of training machine learning algorithms to predict quantum cascade laser designs with high optical gain, a model dataset containing 2600 designs was collected, filtered, and prepared using a representative 8.2 μm laser.

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Hernandez AC, Lyu M, Gmachl CF. Generating Quantum Cascade Laser Datasets for Applications in Machine Learning. In 2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings). doi: 10.1109/SUM53465.2022.9858281

Generating Quantum Cascade Laser Datasets for Applications in Machine Learning

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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