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 proceedingConference contribution

2 Scopus citations

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 languageEnglish (US)
Title of host publication2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665434898
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Cabo San Lucas, Mexico
Duration: Jul 11 2022Jul 13 2022

Publication series

Name2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022 - Proceedings

Conference

Conference2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022
Country/TerritoryMexico
CityCabo San Lucas
Period7/11/227/13/22

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

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