Fully Connected Feedforward Neural Network for the Prediction of Amorphous Silicon Grating Couplers Efficiency

Abstract

Photonic circuits are an enabling technology for the development of novel solutions in different fields such as healthcare, quantum computing, neural networks, communications, and manufacturing. Interconnections between devices and systems require low-loss light coupling strategies. Grating couplers are a promising solution to couple light between photonic circuits and optical fibres due to their off-plane coupling capabilities. Hydrogenated amorphous silicon (a-Si:H), which can be deposited by PECVD over a substrate of silica or glass, is a suitable low-cost solution for the production of such light coupling devices. In this work we developed, trained and tested a fully connected feedforward neural network for coupling efficiency prediction in a-Si:H grating couplers. The light coupling gratings were simulated by twodimensional finite-difference time-domain (FDTD) analysis and field distributions were analysed with the Finite Element Method (FEM). Simulated gratings include non-apodized, linear and quadratic refractive index variation designs featuring full or partial etching, operating at 1550 nm. Not featuring any type of bottom reflector, the couplers exhibit coupling efficiencies up to about 40 % (~ -4 dB). The neural network multiclass grating coupler efficiency classifier was trained with over 3000 simulation results, reaching an accuracy over 85%, for coupling efficiencies between 0 and 30%+.