Performance Enhancement Reservoir Computing System Based on Combination of VCESL Optical Feedback and Mutual Injection Structure

In this paper, a novel performance enhancement reservoir computing (RC) system based on the combination of vertical-cavity surface emitting laser (VCSEL) optical feedback and mutual injection (OFAI) structure is proposed and demonstrated numerically. By simultaneously introducing optical feedback and mutual injection structures into the proposed RC system, the nonlinear and high-dimensional mapping capabilities are significantly improved. The proposed system exhibits the best performance in both single task processing mode and parallel processing mode compared to the other 4 RC systems. Specifically, the minimum NMSE of Santa-Fe time series prediction, waveform classification and NARMA-10 task are 0.0011, 1.058<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 10^{-8}$</tex-math></inline-formula> and 0.101 respectively. Furthermore, since two linear polarization modes coexist in VCSELs, the parallel-polarized and orthogonal-polarized configuration is considered. Numerical results show that in all benchmark tasks, the performance of the orthogonal-polarized configuration is generally better than the parallel-polarized configuration in single task processing mode, and the conclusion is opposite in parallel processing mode, which is related to the coupling mechanism between the two polarization modes. Finally, the effect of different parameters on the system performance is explored in detail. In summary, the proposed system is interesting and valuable in the field of high-speed and low-power neuromorphic photonics.

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Publication Details

Journal
IEEE Journal of Selected Topics in Quantum Electronics
Published
2024-10-14
DOI
https://doi.org/10.1109/jstqe.2024.3480455
Citations
4
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.39

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article

Performance Enhancement Reservoir Computing System Based on Combination of VCESL Optical Feedback and Mutual Injection Structure

Yuefeng Ji, Hongxiang Wang, Pengjin Zhu
4 citations
IEEE Journal of Selected Topics in Quantum Electronics
Neural Networks and Reservoir Computing
1.39
article

Performance Enhancement Reservoir Computing System Based on Combination of VCESL Optical Feedback and Mutual Injection Structure

Yuefeng Ji, Hongxiang Wang, Pengjin Zhu
article en
4 citations

Abstract

In this paper, a novel performance enhancement reservoir computing (RC) system based on the combination of vertical-cavity surface emitting laser (VCSEL) optical feedback and mutual injection (OFAI) structure is proposed and demonstrated numerically. By simultaneously introducing optical feedback and mutual injection structures into the proposed RC system, the nonlinear and high-dimensional mapping capabilities are significantly improved. The proposed system exhibits the best performance in both single task processing mode and parallel processing mode compared to the other 4 RC systems. Specifically, the minimum NMSE of Santa-Fe time series prediction, waveform classification and NARMA-10 task are 0.0011, 1.058<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 10^{-8}$</tex-math></inline-formula> and 0.101 respectively. Furthermore, since two linear polarization modes coexist in VCSELs, the parallel-polarized and orthogonal-polarized configuration is considered. Numerical results show that in all benchmark tasks, the performance of the orthogonal-polarized configuration is generally better than the parallel-polarized configuration in single task processing mode, and the conclusion is opposite in parallel processing mode, which is related to the coupling mechanism between the two polarization modes. Finally, the effect of different parameters on the system performance is explored in detail. In summary, the proposed system is interesting and valuable in the field of high-speed and low-power neuromorphic photonics.

IEEE Journal of Selected Topics in Quantum ElectronicsVol. 31(3: AI/ML Integrated Opto)
Beijing University of Posts and Telecommunications (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 15%
Neural Networks and Reservoir Computing
1.39
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Performance Enhancement Reservoir Computing System Based on Combination of VCESL Optical Feedback and Mutual Injection Structure — Yuefeng Ji, Hongxiang Wang, et al. · IEEE Journal of Selected Topics in Quantum Electronics (2024) | TGRS Research Map | TGRS