Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Deep Learning-based Extraction of Auger and FCA Coefficients in 850 nm GaAs/AlGaAs Laser Diodes

  • Jung-Tack Yang (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Hyewon Han (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Woo-Young Choi (Department of Electrical and Electronic Engineering, Yonsei University)
  • Received : 2023.10.30
  • Accepted : 2024.01.18
  • Published : 2024.02.25
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Abstract

Numerical values of the Auger coefficient and the free carrier absorption (FCA) coefficient are extracted by applying deep neural networks (DNNs) to the L-I characteristics of 850 nm GaAs/AlGaAs laser diodes. Two elemental DNNs are used to extract each coefficient sequentially. The fidelity of the extracted values is established through meticulous correlation of L-I characteristics bridging the realms of simulations and measurements. The methodology presented in this paper offers a way to accurately extract the Auger and FCA coefficients, which were traditionally treated as fitting parameters. It is anticipated that this approach will be applicable to other types of opto-electronic devices as well.

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Acknowledgement

The authors received no financial support for the research, authorship, and publication of this article.

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