MODELING, OPTIMIZATION AND AI-FORECASTING TECHNOLOGY IN RAMAN SPECTROMETRY

Abstract

In the work, the technology of modeling, optimization and prediction of spectral characteristics of thin films based on Raman spectroscopy with the use of artificial intelligence was developed. Modern machine learning methods are implemented, including ensemble algorithms (random forest, gradient boosting) and neural networks, which ensures high accuracy of forecasts and automation of spectrum analysis. An innovative approach includes the use of the Voight profile, which combines Lorentzian and Gaussian components, allowing to describe accurately the width and shape of the peaks for approximation, taking into account the physicochemical parameters of the films and the influence of experimental conditions. The task is caused by the growing requirements for the accuracy of material analysis in the fields of optoelectronics, photocatalysis and sensor systems, where Raman spectroscopy is an indispensable tool. Traditional data processing methods are limited by the complexity of the interaction of light with the material, and the integration of AI allows you to overcome these difficulties, optimizing analysis and prediction. The proposed technology combines physical modeling of spectra with AI-prediction, which allows accurate consideration of the effects of defects, inhomogeneities, and absorption. Algorithms for model optimization with minimization of root mean square error and selection of the best model for specific problems have been implemented. Additional optimization of the model takes into account the influence of the film thickness due to the absorption coefficient and the suppression of unwanted reactions with the help of buffer gases (Ne, Ar). The developed approach provides reduction of time and resources for experimental research, automation of spectrum analysis and development of new materials. The application of AI methods allows obtaining highly accurate results even with a small amount of experimental data. The prospects for technology development include the integration of multilayer structures, consideration of material anisotropy, and detailed modeling of defects in films. Additionally, it can be adapted to analyze different types of materials, such as organic films or hybrid structures. Software enhancements can include automation of spectrum fitting, optimization of film parameters, and machine learning-based property prediction with large data sets. This opens up new opportunities for research in the physico-chemistry of materials and the development of intelligent analysis systems.