scholarly journals Effect of Bias Current on Complexity and Time Delay Signature of Chaos in Semiconductor Laser With Time-Delayed Optical Feedback

2017 ◽  
Vol 23 (6) ◽  
pp. 1-6 ◽  
Author(s):  
Songkun Ji ◽  
Yanhua Hong
2017 ◽  
Vol 27 (11) ◽  
pp. 1750169 ◽  
Author(s):  
Liyue Zhang ◽  
Wei Pan ◽  
Penghua Mu ◽  
Xiaofeng Li ◽  
Shuiying Xiang ◽  
...  

The important role of parameters in master laser with optical feedback for the elimination of time-delay (TD) signature in semiconductor laser subject to chaotic optical injection is investigated systemically. The experimental results show that TD signature suppressed chaotic signals can be credibly generated by increasing the feedback strength of the master laser, which is quite different from the trends observed in semiconductor laser (SL) with optical feedback. Systematically numerical analysis is also carried out as a validation, and it is shown that with low bias current and strong feedback strength, parameter regions contributing to successful TD suppression are much wider. Furthermore, it is shown that the influence of frequency detuning in TD concealment will change with the increase of feedback strength. All the numerical results are in perfect accordance with experimental observation.


2009 ◽  
Vol 79 (3) ◽  
Author(s):  
A. Loose ◽  
B. K. Goswami ◽  
H.-J. Wünsche ◽  
F. Henneberger

2010 ◽  
Vol 18 (7) ◽  
pp. 6661 ◽  
Author(s):  
Jia-Gui Wu ◽  
Guang-Qiong Xia ◽  
Xi Tang ◽  
Xiao-Dong Lin ◽  
Tao Deng ◽  
...  

2009 ◽  
Vol 45 (7) ◽  
pp. 879-1891 ◽  
Author(s):  
Damien Rontani ◽  
Alexandre Locquet ◽  
Marc Sciamanna ◽  
David S. Citrin ◽  
Silvia Ortin

2016 ◽  
Vol 42 (1) ◽  
pp. 163 ◽  
Author(s):  
D. Brunner ◽  
R. Luna ◽  
A. Delhom i Latorre ◽  
X. Porte ◽  
I. Fischer

Photonics ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 47
Author(s):  
Xavier Porte ◽  
Daniel Brunner ◽  
Ingo Fischer ◽  
Miguel C. Soriano

Semiconductor lasers can exhibit complex dynamical behavior in the presence of external perturbations. Delayed optical feedback, re-injecting part of the emitted light back into the laser cavity, in particular, can destabilize the laser’s emission. We focus on the emission properties of a semiconductor laser subject to such optical feedback, where the delay of the light re-injection is large compared to the relaxation oscillations period. We present an overview of the main dynamical features that emerge in semiconductor lasers subject to delayed optical feedback, emphasizing how to experimentally characterize these features using intensity and high-resolution optical spectra measurements. The characterization of the system requires the experimentalist to be able to simultaneously measure multiple time scales that can be up to six orders of magnitude apart, from the picosecond to the microsecond range. We highlight some experimental observations that are particularly interesting from the fundamental point of view and, moreover, provide opportunities for future photonic applications.


Photonics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 124 ◽  
Author(s):  
Krishan Harkhoe ◽  
Guy Van der Sande

Reservoir computing has rekindled neuromorphic computing in photonics. One of the simplest technological implementations of reservoir computing consists of a semiconductor laser with delayed optical feedback. In this delay-based scheme, virtual nodes are distributed in time with a certain node distance and form a time-multiplexed network. The information processing performance of a semiconductor laser-based reservoir computing (RC) system is usually analysed by way of testing the laser-based reservoir computer on specific benchmark tasks. In this work, we will illustrate the optimal performance of the system on a chaotic time-series prediction benchmark. However, the goal is to analyse the reservoir’s performance in a task-independent way. This is done by calculating the computational capacity, a measure for the total number of independent calculations that the system can handle. We focus on the dependence of the computational capacity on the specifics of the masking procedure. We find that the computational capacity depends strongly on the virtual node distance with an optimal node spacing of 30 ps. In addition, we show that the computational capacity can be further increased by allowing for a well chosen mismatch between delay and input data sample time.


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