Resonance prediction and inverse design of multi-core selective couplers based on neural networks

Abstract

Resonance analysis and structural optimization of multi-channel selective fiber couplers currently rely on numerical simulation and manual trial and error, which is very repetitive and time consuming. To realize fast and accurate resonance analysis and calculation, we start with dual-core structures and establish forward classification and regression neural networks to classify and predict different resonance properties, including resonance types, operating wavelength, coupling coefficient, coupling length, 3 dB bandwidth, and conversion efficiency. The pre-trained forward neural networks for dual-core fibers can also realize accurate and fast prediction for multi-core fibers if the mode energy exchange occurs only between one surrounding core and the central core. For the inverse design, a tandem neural network has been constructed by cascading the pre-trained forward neural network and the inverse network to solve the non-uniqueness problem and provide an approach to search for appropriate and desired multi-core structures. The proposed forward and inverse neural networks are efficient and accurate, which provides great convenience for resonance analysis and structural optimization of multi-channel fiber structures and devices.