Open AccessUnevenly spaced continuous measurement approach for dual rotating–retarder Mueller matrix ellipsometry
Author(s) -
Kai Meng,
Bo Jiang,
Christos D. Samolis,
Mohamad Alrished,
Kamal YoucefToumi
Publication year - 2019
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.014736
Subject(s) - robustness (evolution) , computer science , sampling (signal processing) , ellipsometry , retarder , matrix (chemical analysis) , algorithm , optics , materials science , physics , biochemistry , chemistry , thin film , filter (signal processing) , composite material , computer vision , gene , nanotechnology
In order to efficiently extract the sample Mueller matrix by dual rotating-retarder ellipsometry, it is critical for the data reduction technique to achieve a minimal data processing burden while considering the ease of retarder control. In this paper, we propose an unevenly spaced sampling strategy to reach a globally optimal measurement matrix with minimum sampling points for continuous measurements. Taking into account the robustness to both systematic errors and detection noise, we develop multi-objective optimization models to identify the optimal unevenly spaced sampling points. A combined global search algorithm based on the multi-objective genetic algorithm is subsequently designed to solve our model. Finally, simulations and experiments are conducted to validate our approach as well as to provide near-optimal schemes for different design scenarios. The results demonstrate that significant improvement on error immunity performance can be achieved by applying an unevenly sampled measurement strategy compared to an evenly sampled one for our ellipsometer scenario.