Neural network-based analysis algorithm on Mueller matrix data of spectroscopic ellipsometry for the structure evaluation of nanogratings with various optical constants

Abstract

Accurate and fast characterization of nanostructures using spectroscopic ellipsometry (SE) is required in both industrial and research fields. However, conventional methods used in SE data analysis often face challenges in balancing accuracy and speed, especially for the in situ monitoring on complex nanostructures. Additionally, optical constants are so crucial for accurately predicting structural parameters since SE data were strongly related to them. This study proposes a three-step algorithm designed for fast and accurate extraction of structural parameters from SE measurements. The method utilizes three neural networks, each trained on simulation data, to obtain optical constants and progressively refine the prediction on structural parameters at each step. When tested on both simulation and measurement data on the fabricated 1D SiO2 nanograting specimen, the algorithm demonstrated both high accuracy and fast analysis speed, with average mean absolute error (MAE) of 0.103 nm and analysis speed of 132 ms. Also, the proposed algorithm shows more flexibility in accounting for any change of optical constants to serve as a more efficient solution in the real-time monitoring.