In the silicon-based devices, the third-order nonlinear effects containing Kerr effects, two-photon absorption (TPA), free carrier absorption (FCA) and free carrier dispersion (FCD) play the important role in the physical characteristics. In this paper, taking consideration of the linear and nonlinear effects, a comprehensive numerical analysis model based on the finite-difference-time-domain (FDTD) method is built and demonstrated. The nonlinear characteristics of the silicon-based waveguides and micro ring resonators are further discussed in the simulated results. The proposed model might provide an effective analysis method for all silicon-based devices, due to its good compatibility and accuracy.
KEYWORDS: Sensors, Signal to noise ratio, Wavelets, Temperature metrology, Convolutional neural networks, Sensing systems, Reflectometry, Optical fibers, Modulation, Signal processing
We propose an instantaneous temperature measurement method based on wavelet convolutional neural network (wavelet-CNN) to extract Brillouin frequency shift (BFS) in Brillouin optical time domain reflectometer (BOTDR) sensor and map the BFS to the temperature. Compared to Lorentzian curve fitting (LCF), both the simulation and experimental results show the wavelet-CNN has better accuracy and shorter processing time.
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