Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 12, 2026

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies
09:38

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies

Published on: December 18, 2015

Machine Learning Models for Predicting Key Performance Characteristics of High-Temperature THz Quantum Cascade

Mihailo Stojković1, Novak Stanojević1, Aleksandar Milićević2

  • 1School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia.

Nanomaterials (Basel, Switzerland)
|June 11, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Roadmap for light interaction with biophotonic surfaces and their diverse applications.

Journal of biomedical optics·2026
Same author

Subwavelength 3D terahertz imaging with a single-pixel laser transceiver.

Optics express·2025
Same author

Re-calculation of air kerma to dose-equivalent conversion coefficients for mono-energetic photons.

Journal of radiological protection : official journal of the Society for Radiological Protection·2025
Same author

Effects of background doping, interdiffusion and layer thickness fluctuation on the transport characteristics of THz quantum cascade lasers.

Scientific reports·2024
Same author

Resonant Tunnelling and Intersubband Optical Properties of ZnO/ZnMgO Semiconductor Heterostructures: Impact of Doping and Layer Structure Variation.

Materials (Basel, Switzerland)·2024
Same author

Coherent terahertz laser feedback interferometry for hydration sensing in leaves.

Optics express·2023
Same journal

Correction: Jiang et al. Methods for Obtaining One Single Larmor Frequency, Either <i>v</i><sub>1</sub> or <i>v</i><sub>2</sub>, in the Coherent Spin Dynamics of Colloidal Quantum Dots. <i>Nanomaterials</i> 2023, <i>13</i>, 2006.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Correction: Ekman et al. Synthesis, Characterization, and Adsorption Properties of Nitrogen-Doped Nanoporous Biochar: Efficient Removal of Reactive Orange 16 Dye and Colorful Effluents. <i>Nanomaterials</i> 2023, <i>13</i>, 2045.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>-Based Materials and Coatings for De-Icing and Defogging of Wind Turbine Blades: Materials Basis, Structural Design, Engineering Integration, and Future Opportunities.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Influence of the Ripeness Stages of the Precursors on the Optical Characteristics of Carbon Dots Obtained from Valencia Orange Peels (<i>Citrus sinensis</i> L. Osbeck) by Hydrothermal Synthesis.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Insights into ALD Growth of Al-Based Dielectric Stack on 4H-SiC.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Metal-<i>N</i>-Heterocyclic Carbene Porous Organic Polymers as Efficient Bifunctional Water-Splitting Electrocatalysts.

Nanomaterials (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

Machine learning models, including Artificial Neural Networks (ANN), accurately predict quantum cascade laser (QCL) performance. This approach accelerates the optimization of QCL designs by enabling rapid evaluation of millions of configurations.

Area of Science:

  • Semiconductor device physics
  • Computational modeling
  • Machine learning applications

Background:

  • Quantum cascade lasers (QCLs) are crucial optoelectronic devices.
  • Predicting QCL performance characteristics (material gain, current density, emission frequency) typically requires computationally intensive simulations.
  • Optimizing QCL design involves navigating a high-dimensional configuration space.

Purpose of the Study:

  • To develop a machine learning (ML)-based surrogate modeling framework for predicting QCL performance characteristics.
  • To enable faster evaluation and optimization of QCL designs compared to traditional simulation methods.
  • To demonstrate the effectiveness of ML models in predicting device performance and identifying optimal designs.

Main Methods:

Keywords:
Artificial Neural Networksmachine learningterahertz quantum cascade laser

More Related Videos

Construction and Characterization of External Cavity Diode Lasers for Atomic Physics
09:10

Construction and Characterization of External Cavity Diode Lasers for Atomic Physics

Published on: April 24, 2014

Related Experiment Videos

Last Updated: Jun 12, 2026

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies
09:38

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies

Published on: December 18, 2015

Construction and Characterization of External Cavity Diode Lasers for Atomic Physics
09:10

Construction and Characterization of External Cavity Diode Lasers for Atomic Physics

Published on: April 24, 2014

  • Applied Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) for predictive modeling.
  • Generated training data using a numerical simulator based on the density-matrix transport model.
  • Evaluated model performance using standardized Root Mean Square Error (RMSE) values.

    • Main Results:

    • ANN models demonstrated superior predictive performance with the lowest RMSE values (0.04 to 0.22) across all investigated characteristics.
    • The ANN framework enabled the evaluation of approximately 44 million configurations in about five minutes, achieving a ~90,000x speedup.
    • Identified QCL designs with improved material gain and facilitated efficient parameter optimization.

    Conclusions:

    • Combining physics simulations with ML, specifically ANNs, provides a reliable and highly efficient method for predicting QCL performance.
    • The ML-based surrogate modeling framework significantly accelerates the design and optimization process for QCLs.
    • This approach facilitates the exploration of vast design spaces, leading to improved device performance and faster innovation in QCL technology.