Phase-Modulated Ellipsometry Based on Hybrid Algorithm for Non-Calibration Film Thickness Measurement

A phase-modulated ellipsometer enables non-contact, high-precision determination of thin-film optical parameters and thickness through polarized light modulation analysis. However, conventional ellipsometers suffer from limited measurement accuracy due to systemic calibration drift and environmental interference. This research presents a novel metrological approach integrating backpropagation neural networks (BP) with a hybrid Particle Swarm Optimization–Levenberg–Marquardt (PSO-LM) algorithm for thin-film thickness quantification. The proposed framework simultaneously determines system parameters and ellipsometry coefficients (ψ, Δ) via multi-objective optimization, achieving calibration-free thickness characterization with sub-nanometer precision. Experimental validation was performed on SiO2/Si samples with thicknesses ranging from 20 nm to 500 nm. Results demonstrate that the proposed method achieves a root mean square error (RMSE) of <0.006 across the entire thickness range, outperforming the traditional calibration-based method (RMSE ~ 0.008). In addition, the adaptability and stability of the algorithm to complex optical systems are also verified, providing a new method for industrial film thickness monitoring.