Lithium niobate thin film grating couplers optimized by particle swarm optimization and a neural network

Grating couplers (GCs) are a kind of critical device for integrated photonics, which connect on- and off-chip devices. In this paper, chirped GCs on Z-cut lithium niobate on insulator were designed and optimized using a backward propagation neural network (BPNN) combined with the particle swarm optimization (PSO) algorithm. The BPNN was proposed to predict the coupling efficiency (CE) of chirped GCs at hundreds of wavelengths simultaneously, which is 7400 times faster than finite difference time domain simulation. Furthermore, PSO was employed to search for the GC structures with high CE. The maximum CE that can be optimized through our trained network reaches 63% in 1550 nm. This work provides a fast and accurate method for designing efficient GCs at any central wavelength.