Optimization of beam shaping for ultrasensitive inertial measurement using a phase-only spatial light modulator

Xu Chen; Xiujie Fang; Danyue Ma; Ying Liu; Ying Liu; Li Cao; Yueyang Zhai; Yueyang Zhai; Yueyang Zhai

doi:10.1364/AO.441418

Applied Optics
Vol. 61,
Issue 6,
pp. C55-C64
(2022)
•https://doi.org/10.1364/AO.441418

Optimization of beam shaping for ultrasensitive inertial measurement using a phase-only spatial light modulator

Xu Chen, Xiujie Fang, Danyue Ma, Ying Liu, Li Cao, and Yueyang Zhai

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Xu Chen, Xiujie Fang, Danyue Ma, Ying Liu, Li Cao, and Yueyang Zhai, "Optimization of beam shaping for ultrasensitive inertial measurement using a phase-only spatial light modulator," Appl. Opt. 61, C55-C64 (2022)

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About this Article
History
- Original Manuscript: August 30, 2021
- Revised Manuscript: October 16, 2021
- Manuscript Accepted: October 25, 2021
- Published: November 17, 2021
Virtual Issues
Applied Optics Radiography, Applied Optics and Data Science (2022)

Abstract

This study proposes an approach to generate a uniform flat-top beam with a liquid crystal spatial light modulator (LC-SLM) to optimize ultrasensitive inertial measurement. The random incomplete Gaussian beam is modulated into a flat-top beam by uploading a beam shaping optimization algorithm on an LC-SLM. Simulation results verify the effectiveness of the proposed method. The beam obtained from the experimental results with the 4f filter system optimization also conforms to the properties of the generated flat-top beam. Compared to existing beam shaping algorithms for simulation and experimental analysis, the beam shaping design based on the LC-SLM to optimize the ultrasensitive inertial measurement is realized. This method has also been verified to be effective in beam shaping in various beam situations. The application of this method in ultrahigh-sensitivity inertial measurement should prove significant.

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Data Availability

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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	$e (%)$	$σ (%)$	$η (%)$
Before 4f filtering	64.69	59.74	63.51
After 4f filtering	30.40	69.25	50.99

	$e (%)$	$σ (%)$	$η (%)$
$w_{1} = w_{0} / 5$	11.69	96.21	18.07
$w_{1} = w_{0} / 3$	3.56	89.95	19.18
$w_{1} = w_{0} / 2$	1.03	79.23	23.56
$w_{1} = 4 w_{0} / 5$	20.68	57.27	57.27
$w_{1} = w_{0}$	49.24	44.14	61.70

	$e (%)$	$σ (%)$	$η (%)$
Gerchberg–Saxton algorithm	5.67	59.68	17.81
Genetic algorithm	6.96	59.69	17.64
Simulated annealing	6.78	58.56	15.58
Improved optimization algorithm	4.23	58.84	12.89

	$e (%)$	$σ (%)$	$η (%)$
Before 4f filtering	64.69	59.74	63.51
After 4f filtering	30.40	69.25	50.99

	$e (%)$	$σ (%)$	$η (%)$
$w_{1} = w_{0} / 5$	11.69	96.21	18.07
$w_{1} = w_{0} / 3$	3.56	89.95	19.18
$w_{1} = w_{0} / 2$	1.03	79.23	23.56
$w_{1} = 4 w_{0} / 5$	20.68	57.27	57.27
$w_{1} = w_{0}$	49.24	44.14	61.70

Abstract

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Tables (3)

Equations (15)

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