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.

References

  1. P. Crump, G. Erbert, H. Wenzel, C. Frevert, C. M. Schultz, K.-H. Hasler, R. Staske, B. Sumpf, A. Maassdorf, F. Bugge, S. Knigge, and G. Trankle, "Efficient high-power laser diodes," IEEE J. Sel. Top. Quantum Electron. 19, 1501211-1501211 (2013).
  2. D. Xu, Z. Guo, T. Zhang, K. Song, W. Guo, B. Wang, R. Xu, and X. Chen, "600 W high brightness diode laser pumping source," Proc. SPIE 10086, 1008603 (2017).
  3. M. Kelemen, J. Gilly, P. Friedmann, S. Hilzensauer, L. Ogrodowski, H. Kissel, and J. Biesenbach, "Diode lasers optimized in brightness for fiber laser pumping," Proc. SPIE 10514, 10514F (2018).
  4. T. H. Kim, N.-J. Kim, and J.-I. Youn, "Evaluation of wavelength-dependent hair growth effects on low-level laser therapy: An experimental animal study," Lasers Med. Sci. 30, 1703-1709 (2015).
  5. V. Knappe, F. Frank, and E. Rohde, "Principles of lasers and biophotonic effects," Photomed. Laser Surg. 22, 411-417 (2004).
  6. H. M. Oubei, C. Li, K.-H. Park, T. K. Ng, M.-S. Alouini, and B. S. Ooi, "2.3 Gbit/s underwater wireless optical communications using directly modulated 520 nm laser diode," Opt. Express 23, 20743-20748 (2015).
  7. H. Kawaguchi, "Bistable laser diodes and their application: State of the art," IEEE J. Sel. Top. Quantum Electron. 3, 1254-1270 (1997).
  8. T. Zheng, G. Shen, Z. Li, L. Yang, H. Zhang, E. Wu, and G. Wu, "Frequency-multiplexing photon-counting multi-beam LiDAR," Photonics Res. 7, 1381-1385 (2019).
  9. I. Vornicu, J. M. Lopez-Martinez, F. N. Bandi, R. C. Galan, and A. Rodriguez-Vazquez, "Design of high-efficiency SPADs for LiDAR applications in 110 nm CIS technology," IEEE Sens. J. 21, 4776-4785 (2021).
  10. J.-E Joo, M.-J. Lee, and S. M. Park, "A CMOS fully differential optoelectronic receiver for short-range LiDAR sensors," IEEE Sens. J. 23, 4930-4939 (2023).
  11. S. I. Bae, S. Lee, J.-M. Kwon, H.-K. Kim, K.-W. Jang, D. Lee, and K.-H. Jeong, "Machine-learned light-field camera that reads facial expression from high-contrast and illumination invariant 3D facial image," Adv. Intell. Syst. 4, 2100182 (2021).
  12. J. Cheng, X. Sun, S. Zhou, X. Pu, N. Xu, Y. Xu, and W. Liu, "Ultra-compact structured light projector with all-dielectric metalenses for 3D sensing," AIP Adv. 9, 105016 (2019).
  13. Y. Rivenson, Y. Zhang, H. Gunaydin, D. Teng, and A. Ozcan, "Phase recovery and holographic image reconstruction using deep learning in neural networks," Light Sci. Appl. 7, 17141 (2017).
  14. B. Yao, W. Li, W. Pan, Z. Yang, D. Chen, J. Li, and J. Qu, "Image reconstruction with a deep convolutional neural network in high-density super-resolution microscopy," Opt. Express 28, 15432-15446 (2020).
  15. T. Koike-Akino, Y. Wang, D. S. Millar, K. Kojima, and K. Parsons, "Neural turbo equalization: Deep learning for fiber-optic nonlinearity compensation," J. Light. Technol. 38, 3059-3066 (2020).
  16. B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, "Multimode optical fiber transmission with a deep learning network," Light Sci. Appl. 7, 69 (2018).
  17. X. Han, Z. Fan, Z. Liu, C. Li, and L. J. Guo, "Inverse design of metasurface optical filters using deep neural network with high degrees of freedom," InfoMat 3, 432-442 (2020).
  18. T. Zhao, W. Ji, P. Liu, F. Gao, C. Li, Y. Wang, and W. Huang, "Highly efficient inverse design of semiconductor optical amplifiers based on neural network improved particle swarm optimization algorithm," IEEE Photonics J. 15, 8500409 (2023).
  19. Z. Ma, P. Feng, and Y. Li, "Inverse design of semiconductor laser parameters based on deep learning and particle swarm optimization method," Proc. SPIE 11209, 112092X (2019).
  20. Z. Ma and Y. Li, "Parameter extraction and inverse design of semiconductor lasers based on the deep learning and particle swarm optimization method," Opt. Express 28, 21971-21981 (2020).
  21. J. R. Meyer, C. L. Canedy, M. Kim, C. S. Kim, C. D. Merritt, W. W. Bewley, and I. Vurgaftman, "Comparison of Auger coefficients in type I and type II quantum well midwave infrared lasers," IEEE J. Quantum Electron. 57, 2500110 (2021).
  22. J. Piprek, P. Abraham, and J. E. Bowers, "Self-consistent analysis of high-temperature effects on strained-layer multiquantum-well InGaAsP-InP lasers," IEEE J. Quantum Electron. 36, 366-374 (2000).
  23. J. Piprek, P. Abraham, and J. E. Bowers, "Cavity length effects on internal loss and quantum efficiency of multiquantumwell lasers," IEEE J. Sel. Top. Quantum Electron 5, 643-647 (1999).
  24. J. Piprek, J. K. White, and A. J. SpringThorpe, "What limits the maximum output power of long-wavelength AlGaInAs/ InP laser diodes?," IEEE J. Quantum Electron. 38, 1253-1259 (2002).