Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm

Abstract

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).

Low-complexity EVM estimation based on artificial neural networks for coherent optical systems

“…The derivative of the loss is analyzed so that the influence of each contribution of the weight to the overall loss can be evaluated. To reduce mistakes following the derivative rate, optimization methods [22][23][24][25][26][27][28][29][30] are constructed and used to make adjustments to all of the weights. In this manner, the system can discover the correlations between components, and it also constructs a model that is suited to forecasting the characteristics of any formulation or workflow problem that lies inside the model space.…”