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AI-powered deep ultraviolet laser diode design for resource-efficient optimization

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Abstract

AlGaN-based deep ultraviolet laser diodes (LDs) have attracted considerable interest because of their diverse applications over the last two decades. The optimization of DUV LDs is essential to advancing high-efficiency photonic devices. However, traditional simulation tools are computationally intensive and slow, presenting challenges for iterative development of optoelectronic devices. We propose an AI-driven approach that leverages machine learning (ML) and explainable AI (XAI) to accelerate the design process of DUV LD and enhance understanding of the correlation between key LD performance parameters. Our methodology involves training ML models on a dataset of DUV LD design parameters to evaluate each model’s predictive accuracy. We also integrate XAI to assess input feature importance such as material composition and thickness of epilayers. This framework provides predictions for laser output power (), laser threshold current (), and optical confinement factor () with R2 values of 73%, 71%, and 80%, respectively, with the best-performing model that is extreme gradient boosting. This model substantially reduces the computational time required for optimum design iteration. These results demonstrate that our AI-based approach outperforms traditional methods in speed and resource efficiency, providing actionable insights into design parameters that align with physical mechanisms. This work establishes a resource-efficient AI framework that accelerates the development cycle of high-performance LDs.

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Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

  • Diode Lasers
  • Laser Technology
  • Optoelectronic Devices
  • Semiconductor Lasers
  • Semiconductors
  • Artificial Intelligence

Data availability

The data supporting the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors acknowledge the support from the Ghulam Ishaq Khan Institute of Engineering Sciences and Technology for providing resources essential for this research. This work was also supported by the Higher Education Commission (HEC) of Pakistan under the National Research Program for Universities (NRPU), Grant No. 14812.

Funding

This study was funded by Higher Education Commission (HEC) of Pakistan under the National Research Program for Universities (NRPU), Grant No. 14812. The studentship and lab resources were funded by Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (Pakistan).

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan

    Asima Sarwar, Masroor Hussain & Khurram Khan Jadoon

  2. Faculty of Engineering Sciences, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan

    Muhammad Usman & Shazma Ali

  3. Energy Research Centre, COMSATS University Islamabad, Lahore, Pakistan

    Tareq Manzoor

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Correspondence to Asima Sarwar or Muhammad Usman.

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Sarwar, A., Usman, M., Hussain, M. et al. AI-powered deep ultraviolet laser diode design for resource-efficient optimization. J Comput Electron 24, 136 (2025). https://doi.org/10.1007/s10825-025-02352-z

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  • DOI  https://doi.org/10.1007/s10825-025-02352-z

Keywords

  • Deep ultraviolet laser diode
  • Machine learning
  • Explainable AI
  • Optical confinement factor
  • Ensemble models.

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