Forward design method for the design of panda polarization-maintaining few-mode optical fiber based on artificial neural network

Panda polarization-maintaining few-mode optical fiber (PPMFMOF) has important research significance in the short distance optical transmission field owing to its advantages of weak nonlinear effects, which is benefit to reduce the use of digital signal processing equipment. Designing a high-performance PPMFMOF quickly and efficiently is expected and yet challenging. In this article, we demonstrated a forward design method for the design of PPMFMOF based on artificial neural network (ANN) to solve the problems of inefficient and time-consuming PPMFMOF design in traditional design method. By studying the influence of different ANN models on the fiber performance, the approximate range of the optimal value was obtained in advance, then the minimum effective refractive index difference (Δn_eff,min) between adjacent LP modes was used as the optimization object, finally design of PPMFMOF supporting 10 LP modes in C + L band was successfully realized. This method provided low time-consuming, high-efficiency and high-accuracy for the fast design of PPMFMOF and the maximum mean absolute percentage error (MAPE) of the ANN model to predict the effective refractive index (n_eff) of 10 LP modes is only 3.2211 × 10^-7. We believe that the proposed method could also be quickly and accurately applied to other functional optical fiber designs.