Broad-Bandwidth Slow Light in Multi-Line Brillouin Gain Spectrum

Author(s):  
Yongkang Dong ◽  
Zhiwei Lu ◽  
Qiang Li
2021 ◽  
Author(s):  
Neel Choksi ◽  
Yi Liu ◽  
Rojina Ghasemi ◽  
Li Qian

Abstract Ultra-narrow spectral features are desirable for a broad range of applications, and they are conventionally realized using ultrahigh Q resonant structures. These structures typically require precision fabrication processes, and moreover, since they are passive, they suffer from signal loss. Here, we demonstrate a novel way to achieve sub-MHz tunable spectral dip in the Brillouin gain spectrum of a spun birefringent fiber (SBF) without loss, and without using a resonator. We show that this dip is unique to SBF, where its polarization eigenmodes are elliptical and frequency-dependent, and the dip only occurs when these orthogonal polarization eigenmodes of the SBF (at the respective pump and signal frequencies) are launched in counter-propagating directions. We experimentally demonstrate a 0.72 MHz spectral dip in the Brillouin gain spectrum of a commercial SBF which is to our knowledge, the narrowest SBS spectral feature ever reported. Furthermore, the linewidth, depth, and spectral location of this dip are tunable on demand by controlling the pump frequency, pump power, and the input polarization of the signal. Its simplicity in implementation, its ultra-narrow linewidth, and its tunability can have a wide range of potential applications, from slow-light to microwave photonics.


2007 ◽  
Vol 15 (4) ◽  
pp. 1871 ◽  
Author(s):  
Zhiwei Lu ◽  
Yongkang Dong ◽  
Qiang Li

2014 ◽  
Vol 39 (17) ◽  
pp. 5118 ◽  
Author(s):  
Gabriel K. W. Gan ◽  
Y. G. Shee ◽  
K. S. Yeo ◽  
G. Amouzad Madhiraji ◽  
F. R. Mahamd Adikan ◽  
...  

2008 ◽  
Vol 16 (11) ◽  
pp. 8026 ◽  
Author(s):  
Taiji Sakamoto ◽  
Takashi Yamamoto ◽  
Kazuyuki Shiraki ◽  
Toshio Kurashima

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1166
Author(s):  
Bin Liu ◽  
Jianping He ◽  
Shihai Zhang ◽  
Yinping Zhang ◽  
Jianan Yu ◽  
...  

Brillouin frequency shift (BFS) of distributed optical fiber sensor is extracted from the Brillouin gain spectrum (BGS), which is often characterized by Lorenz type. However, in the case of complex stress and optical fiber self damage, the BGS will deviate from Lorenz type and be asymmetric, which leads to the extraction error of BFS. In order to enhance the extraction accuracy of BFS, the Lorenz local single peak fitting algorithm was developed to fit the Brillouin gain spectrum curve, which can make the BSG symmetrical with respect to the Brillouin center frequency shift. One temperature test of a fiber-reinforced polymer (FRP) packaged sensor whose BSG curve is asymmetric was conducted to verify the idea. The results show that the local region curve of BSG processed by the developed algorithm has good symmetry, and the temperature measurement accuracy obtained by the developed algorithm is higher than that directly measured by demodulation equipment. Comparison with the reference temperature, the relative measurement error measured by the developed algorithm and BOTDA are within 4% and 8%, respectively.


2004 ◽  
Vol 22 (2) ◽  
pp. 631-639 ◽  
Author(s):  
Y. Koyamada ◽  
S. Sato ◽  
S. Nakamura ◽  
H. Sotobayashi ◽  
W. Chujo

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 474
Author(s):  
Fen Xiao ◽  
Mingxing Lv ◽  
Xinwan Li

Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.


Author(s):  
Jianqin Peng ◽  
Yuangang Lu ◽  
Zelin Zhang ◽  
Zhengnan Wu ◽  
Yuyang Zhang

Sign in / Sign up

Export Citation Format

Share Document