A Ge-doped dual-core dispersion compensation photonic crystal fiber (DC-DCPCF) is proposed. The small diameters of two layers' air holes make DC-DCPCF form a dual-core structure, which is conducive to broadband dispersion compensation. Low Ge-doped silica as the only background material reduces the preparation difficulty and cost. It is inversely designed by using artificial neural network (ANN) combined with differential evolution algorithm (DE) to obtain target dispersion compensation. ANN replaces the finite element method to accomplish fast forward prediction of DC-DCPCF properties. DE solves the single solution problem of single or cascade network that makes it flexible and reproducible. The results demonstrate that the designed DC-DCPCF can not only compensate 45 and 25 times its length of Corning single-mode fiber 28 (SMF28) in S+C+L+U bands and E+S+C+L+U bands respectively, but also accurately compensate the residual dispersion with effective dispersion compensation being only +0.005∼+0.842ps/(nm•km) and −0.03∼+1.31ps/(nm•km), respectively. In addition, the kappa values of DCP-PCF are well matched with SMF28 in the broadband wavelength range. It takes only about 10 seconds to complete the inverse design of the target DC-DCPCF. It provides a design method for custom DC-DCPCF and an efficient inverse design solution for photonic automation in fiber optical communication systems. Index Terms-Deep learning, differential evolution algorithm, dual-core dispersion compensation fiber, enter keywords or phrases in alphabetical order, inverse design, photonic crystal fiber. I. INTRODUCTIONI N MODERN optical fiber communication systems, especially in long-range transmission systems such as dense wavelength division multiplexing (DWDM), dispersion compensation is of crucial importance [1], [2], [3]. Since the conventional single mode fiber has dispersion accumulation at each wavelength in the optical fiber transmission link, it is necessary to compensate not only the dispersion but also the dispersion slope [4], [5]. In consequence, some designs of dual-core dispersion compensation photonic crystal fiber (DC-DCPCF).
Optical frequency comb (OFC) has important applications in measurement, communication, military and other fields. Usually, OFC needs to be designed according to different applications. However, the existing methods to design the operating parameters of the OFC generators are time-consuming, inefficient, and difficult to achieve optimal results. In this paper, a novel method of inversely designing OFC using deep learning, which is real-time and can improve the performance of the generated OFC, is proposed and applied to an OFC generator based on a single dual-drive Mach-Zehnder modulator. In this method, according to the required target OFC, the trained neural network can be used to inversely design the corresponding parameters. Using this inverse design method, the generated OFC not only is highly consistent with the target OFC, but also has the programmability of comb-line number, comb-line power, side mode suppression ratio, and comb spacing. Moreover, the proposed method can be utilized for more complicated OFC generator, and is an inspiration for efficient design of OFC.
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