Extraction of Silicon Photonic Wafer-Scale Process Variability Using ML-Enhanced Algorithm

An experimental investigation has been conducted to extract the silicon photonic wafer-scale fabrication process variability using a machine-learning enhanced algorithm to predict sub-nanometer scale width, height, and effective index variations $(\Delta n\sim 10^{-5})$ for a single-mode waveguide.