Hybrid algorithm for predicting the temperature-variation-induced wavelength drift of DBR semiconductor lasers

Ziqi Yuan; Yanpei Shi; Shudong Lin; Ziqian Yue; Xiujie Fang; Dong Hu; Yueyang Zhai

doi:10.1364/AO.456863

Applied Optics
Vol. 61,
Issue 25,
pp. 7380-7387
(2022)
•https://doi.org/10.1364/AO.456863

Hybrid algorithm for predicting the temperature-variation-induced wavelength drift of DBR semiconductor lasers

Ziqi Yuan, Yanpei Shi, Shudong Lin, Ziqian Yue, Xiujie Fang, Dong Hu, and Yueyang Zhai

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Ziqi Yuan, Yanpei Shi, Shudong Lin, Ziqian Yue, Xiujie Fang, Dong Hu, and Yueyang Zhai, "Hybrid algorithm for predicting the temperature-variation-induced wavelength drift of DBR semiconductor lasers," Appl. Opt. 61, 7380-7387 (2022)

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Table of Contents Category
- Lasers, Optical Amplifiers, and Laser Optics
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About this Article
History
- Original Manuscript: February 23, 2022
- Revised Manuscript: July 13, 2022
- Manuscript Accepted: July 17, 2022
- Published: August 24, 2022

Abstract

In this paper, a hybrid algorithm to predict the wavelength drift induced by ambient temperature variation in distributed Bragg reflector semiconductor lasers is proposed. This algorithm combines the global search capability of a genetic algorithm (GA) and the supermapping ability of an extreme learning machine (ELM), which not only avoids the randomness of ELM but also improves its generalization performance. In addition, a tenfold cross-validation method is employed to determine the optimal activation function and the number of hidden layer nodes for ELM to construct the most suitable model. After applying multiple sets of test data, the results demonstrate that GA-ELM can quickly and accurately predict the wavelength drift, with an average rms error of $4.09 \times {10^{- 4}}\;{\rm nm}$ and average mean absolute percentage error of 0.21 %. This model is expected to combine the temperature and current tuning models for a wavelength in follow-up research to achieve rapid tuning and high stability of a wavelength without additional devices.

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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	RMSE (nm)	MAPE (%)
ELM	$6.25 \times 10^{- 5}$	$1.51 \times 10^{- 4}$
GA-ELM	$5.64 \times 10^{- 5}$	$2.71 \times 10^{- 5}$

	Model	RMSE (nm)	MAPE (%)
New Data 1	ELM (1)	$6.88 \times 10^{- 3}$	0.85
	ELM (2)	$5.11 \times 10^{- 4}$	0.13
	ELM (3)	$4.89 \times 10^{- 4}$	0.09
	GA-ELM	$4.65 \times 10^{- 4}$	0.12
New Data 2	ELM (1)	$4.76 \times 10^{- 4}$	0.35
	ELM (2)	$3.81 \times 10^{- 4}$	0.27
	ELM (3)	$5.02 \times 10^{- 4}$	0.35
	GA-ELM	$3.41 \times 10^{- 4}$	0.30

	RMSE (nm)	MAPE (%)
ELM	$6.25 \times 10^{- 5}$	$1.51 \times 10^{- 4}$
GA-ELM	$5.64 \times 10^{- 5}$	$2.71 \times 10^{- 5}$

	Model	RMSE (nm)	MAPE (%)
New Data 1	ELM (1)	$6.88 \times 10^{- 3}$	0.85
	ELM (2)	$5.11 \times 10^{- 4}$	0.13
	ELM (3)	$4.89 \times 10^{- 4}$	0.09
	GA-ELM	$4.65 \times 10^{- 4}$	0.12
New Data 2	ELM (1)	$4.76 \times 10^{- 4}$	0.35
	ELM (2)	$3.81 \times 10^{- 4}$	0.27
	ELM (3)	$5.02 \times 10^{- 4}$	0.35
	GA-ELM	$3.41 \times 10^{- 4}$	0.30

Abstract

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