مشخصه يابي و تحليل مُدهاي خروجي يك موجبر با استفاده از تكنيك تجزيه مُدي

Mode analysis an‎d characterization of a waveguide using mode decomposition technique

فيزيك

Optical waveguide technology has experienced significant growth in recent decades as a fundamental infrastructure for developing modern optical an‎d photonic systems. These structures, capable of guiding light in specific media, have found extensive applications in areas such as optical communications, optical sensors, an‎d photonic integrated circuits. One effective approach for analyzing waveguides is the decomposition of the output field into its constituent optical modes. This analysis not only provides a deep understan‎ding of how waves propagate within the waveguide but also enables the extraction of critical physical properties such as intensity, phase, wavefront, beam quality factor, the Poynting vector, an‎d the orbital angular momentum density of the mode. Furthermore, comparing the input an‎d output modes can shed light on phenomena like modal loss an‎d facilitate a detailed analysis of mode coupling. Since, in most laboratory setups, only the intensity profile at the waveguide output is directly measurable an‎d phase information is unavailable, this research performs modal decomposition based solely on the intensity distribution. This approach reduces computational complexity while still allowing for the extraction of modal contributions with adequate accuracy. To this end, first, a channel optical waveguide was modeled an‎d simulated in the COMSOL Multiphysics software, an‎d its guided modes were extracted. Then, the obtained modes were transferred to the Python programming environment, an‎d their corresponding weighting coefficients were calculated using optimization algorithms. Additionally, in the experimental part, a laser light source with a wavelength of 980 nm was coupled into the waveguide via an optical fiber, an‎d the waveguide’s intensity profiles were recorded using a microscopic camera. These data were then subjected to modal decomposition using optimization algorithms. This study employed three optimization algorithms: the Genetic Algorithm (GA), Pattern Search (PS), an‎d Gradient Descent (GD). Each of these algorithms exhibits differences in terms of convergence behavior, intensity profile reconstruction accuracy, an‎d execution time, which were compared an‎d eva‎luated. Comparing the performance of these algorithms not only assesses the efficiency of each method but also aids in selec‎ting the most suitable algorithm for laboratory applications an‎d complex simulations.

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