A Lightweight Neural Network for Spectroscopic Ellipsometry Analysis

Ellipsometry is a widely used technique in thin film characterizations. To extract the optical properties of the films from the measured data, regression data fitting techniques have been developed that iteratively find a set of optical parameters that best fit the observations. However, this iterative process often faces challenges in converging to the correct solution, and it can be time-consuming. To address these issues, an 8-bit quantized lightweight method of neural network analysis for spectroscopic ellipsometry are proposed. This method features compact neural network modules to enhance speed and efficiency. The effectiveness of the approach through experimental verification is validated on a diverse range of material films, including metals, semiconductors, and dielectrics. The approach is fully automatic and lightweight, which offers a new perspective on balancing predictive accuracy with limited computational resources. This method holds the potential to achieve automatic, rapid, and high-throughput optical characterization of films, facilitating real-time quality monitoring for repeatable high-precision film manufacturing. Ellipsometry, a pivotal technique in thin film analysis, often grapples with convergence and time hurdles in optical property extraction. Addressing these issues, an innovative 8-bit quantized neural network approach for spectroscopic ellipsometry is presented. Compact neural modules enhance speed and efficiency, validated across diverse materials. This automated lightweight solution balances predictive accuracy and limited resources, promising automated thin film characterization in high-precision manufacturing.image