A 1.55 μm Wideband 1 × 2 Photonic Power Splitter With Arbitrary Ratio: Characterization and Forward Modeling

Abstract

Chip-based photonic systems have undergone substantial progress over the last decade. However, the realization of photonic devices still depends largely on intuition-based trial-and-error methods, with a limited focus on characteristic analysis. In this work, we demonstrate an in-depth investigation of photonic power splitters by considering the transmission properties of 16,000 unique ultra-compact silicon-based structures engraved with SiO2, Al2O3, and Si3N4 nanoholes. The characterization has been performed using finite-difference time-domain (FDTD) simulations for each dielectric material and both TE and TM polarizations at the fundamental modes in a wideband optical communication spectrum ranging from 1.45 to $1.65~\mu \text{m}$ . The corresponding transmissions, splitting ratio, and reflection loss were calculated, generating a dataset that can be used for both forward and inverse modeling purposes, using Machine Learning (ML) and Deep Learning (DL) algorithms. With an optimized hole radius of 35 nm, the proposed device area footprint of $2\,\,\mu \text{m}\,\,\times 2\,\,\mu \text{m}$ is among the smallest with the best transmission reported to date. Si3N4 holes show excellent transmission because they offer 90% transmittance in 96% of the data while exhibiting maximum fabrication tolerance. Forward modeling analysis, predicting the transmission properties, was performed using both Linear Model (LM) and Artificial Neural Network (ANN), where LM showed marginally better accuracy than ANN in foreseeing the transmittance. The proposed observation will aid in achieving robust, optimized optical power splitters with a wide range of splitting ratios in lesser time.

Keywords

• Forward modeling
• machine learning
• optical power splitter
• artificial neural network