scholarly journals Modal reconstruction of transverse mode-locked laser beams

2020 ◽  
Vol 126 (10) ◽  
Author(s):  
Florian Schepers ◽  
Tim Hellwig ◽  
Carsten Fallnich

Abstract Transverse mode-locking in an end-pumped solid state laser by amplitude modulation with an acousto-optic modulator was investigated. Using the stochastic parallel gradient descent algorithm the modal power coefficients and the modal phases of the transverse mode-locked (TML) laser beam were reconstructed from the measured spatial and spatio-temporal intensity distributions, respectively. The distribution of the reconstructed modal power coefficients revealed that the average mode order of the transverse mode-locking process could be increased by a factor of about 8 compared to previous works, corresponding to an increase in the normalized oscillation amplitude by a factor of about 3. Furthermore, we found that besides a non-Poissonian modal power distribution, strong aberrations of the modal phases occurred in the experiment, resulting in a deformation of the oscillating spot. Additionally, we demonstrated the generation of up to four spots oscillating simultaneously on parallel traces by operating the TML laser on a higher mode order in the orthogonal direction to the transverse mode-locking process. TML lasers are of interest, e.g., for beam scanning purposes, as they have the potential to enable spot resolving rates in the multi-GHz regime.

Photonics ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 165
Author(s):  
Shiqing Ma ◽  
Ping Yang ◽  
Boheng Lai ◽  
Chunxuan Su ◽  
Wang Zhao ◽  
...  

For a high-power slab solid-state laser, obtaining high output power and high output beam quality are the most important indicators. Adaptive optics systems can significantly improve beam qualities by compensating for the phase distortions of the laser beams. In this paper, we developed an improved algorithm called Adaptive Gradient Estimation Stochastic Parallel Gradient Descent (AGESPGD) algorithm for beam cleanup of a solid-state laser. A second-order gradient of the search point was introduced to modify the gradient estimation, and it was introduced with the adaptive gain coefficient method into the classical Stochastic Parallel Gradient Descent (SPGD) algorithm. The improved algorithm accelerates the search for convergence and prevents it from falling into a local extremum. Simulation and experimental results show that this method reduces the number of iterations by 40%, and the algorithm stability is also improved compared with the original SPGD method.


2009 ◽  
Vol 29 (2) ◽  
pp. 431-436 ◽  
Author(s):  
周朴 Zhou Pu ◽  
刘泽金 Liu Zejin ◽  
马阎星 Ma Yanxing ◽  
王小林 Wang Xiaolin ◽  
许晓军 Xu Xiaojun ◽  
...  

2010 ◽  
Vol 30 (10) ◽  
pp. 2874-2878
Author(s):  
王小林 Wang Xiaolin ◽  
周朴 Zhou Pu ◽  
马阎星 Ma Yanxing ◽  
马浩统 Ma Haotong ◽  
许晓军 Xu Xiaojun ◽  
...  

2009 ◽  
Vol 36 (5) ◽  
pp. 1091-1096
Author(s):  
王三宏 Wang Sanhong ◽  
梁永辉 Liang Yonghui ◽  
龙学军 Long Xuejun ◽  
于起峰 Yu Qifeng ◽  
谢文科 Xie Wenke

2015 ◽  
Vol 42 (4) ◽  
pp. 0402004 ◽  
Author(s):  
黄智蒙 Huang Zhimeng ◽  
唐选 Tang Xuan ◽  
刘仓理 Liu Cangli ◽  
李剑峰 Li Jianfeng ◽  
张大勇 Zhang Dayong ◽  
...  

2014 ◽  
Vol 7 (2) ◽  
pp. 260-266
Author(s):  
刘磊 LIU Lei ◽  
郭劲 GUO Jin ◽  
赵帅 ZHAO Shuai ◽  
姜振华 JIANG Zhen-hua ◽  
孙涛 SUN Tao ◽  
...  

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