title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¿ÑÐ¾ÑÐµÑÑÐ° ÑÐ¸Ð½ÑÐµÐ·Ð° ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð¾Ð² Ð¸ ÑÐ¾Ð½ÐºÐ¸Ñ Ð¿Ð»ÐµÐ½Ð¾Ðº
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Application of machine learning methods to analyze the synthesis process of crystals and thin films
Creators: name: Ð Ð¾Ð¶ÐµÐ½ÑÐµÐ², ÐÐ°Ð½Ð¸Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ñ
ÑÐ¾Ð½ÐºÐ¸Ðµ Ð¿Ð»ÐµÐ½ÐºÐ¸
ÑÐ¸Ð½ÑÐµÐ·
ÐºÐ°ÑÐ±Ð¸Ð´ ÐºÑÐµÐ¼Ð½Ð¸Ñ
ÐºÑÐµÐ¼Ð½Ð¸Ð¹
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼Ð½Ð¾Ð³Ð¾Ð¼ÐµÑÐ½Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
crystals
thin films
synthesis
silicon carbide
silicon
machine learning
multivariate regression
gradient boosting

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ±Ð¾ÑÑ, Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸ Ð¾Ð±Ð¾Ð±ÑÐµÐ½Ð¸Ñ Ð²ÑÐµÑ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ðº Ð½Ð°ÑÑÐ¾ÑÑÐµÐ¼Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð¼Ð°ÑÑÐ¸Ð²Ð¾Ð² ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿Ð¾ ÑÐ¸Ð½ÑÐµÐ·Ñ ÑÐ¾Ð½ÐºÐ¸Ñ Ð¿Ð»ÐµÐ½Ð¾Ðº SiC/Si Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð·Ð°Ð¼ÐµÑÐµÐ½Ð¸Ñ Ð°ÑÐ¾Ð¼Ð¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¸ Ð½Ð°ÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ ÑÐºÑÑÑÑÑ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐµÐ¹ Ð² Ð½Ð¸Ñ, Ð¸ ÑÐ¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ³Ð¾ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð°. ÐÐ»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ Ð¸Ð· ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð±ÑÐ»Ð¸ Ð¸Ð·Ð¼ÐµÑÐµÐ½Ñ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð° Ð¿Ð¾Ð»ÑÑÐ¸Ð²ÑÐ¸ÑÑÑ ÑÐ»Ð¾ÐµÐ², ÑÐ°ÐºÐ¸Ð¼ Ð¾Ð±ÑÐ°Ð·Ð¾Ð¼ Ð±ÑÐ» Ð¿Ð¾Ð»ÑÑÐµÐ½ Ð±Ð¾Ð»ÑÑÐ¾Ð¹ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ, Ð² ÐºÐ¾ÑÐ¾ÑÐ¾Ð¼ Ð²ÑÐ¾Ð´Ð½ÑÐ¼ ÑÐ¾ÑÑÐ¾Ð²ÑÐ¼ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ°Ð¼ ÑÐ¾Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð¸ÑÐ¾Ð³Ð¾Ð²ÑÐµ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð° Ð²ÑÑÐ°ÑÐµÐ½Ð½Ð¾Ð¹ Ð¿Ð»ÐµÐ½ÐºÐ¸. Ð Ð½Ð°ÑÑÐ¾ÑÑÐµÐ¹ ÑÐ°Ð±Ð¾ÑÐµ Ð´Ð»Ñ Ð°Ð¿Ð¿ÑÐ¾ÐºÑÐ¸Ð¼Ð°ÑÐ¸Ð¸ Ð¸ Ð¾Ð±Ð¾Ð±ÑÐµÐ½Ð¸Ñ Ð²ÑÐµÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð² ÑÐ¾Ð¼ ÑÐ¸ÑÐ»Ðµ â ÑÐµÐ³ÑÐµÑÑÐ¸Ñ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ Ð±ÑÑÑÐ¸Ð½Ð³Ð°, Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð¹ Ð¸Ð´ÐµÐµÐ¹ ÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ ÑÐ²Ð»ÑÐµÑÑÑ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ»Ð°Ð±ÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, Ð¿Ð¾Ð»ÑÑÐ°ÐµÐ¼ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑÐ°, Ð³Ð´Ðµ Ð½Ð° ÐºÐ°Ð¶Ð´Ð¾Ð¹ Ð¸ÑÐµÑÐ°ÑÐ¸Ð¸ Ð½Ð¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾Ð±ÑÑÐ°ÐµÑÑÑ Ñ ÑÑÐµÑÐ¾Ð¼ Ð¾ÑÐ¸Ð±Ð¾Ðº Ð¿ÑÐµÐ´ÑÐ´ÑÑÐ¸Ñ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ²Ð¸Ð´ÐµÑÐµÐ»ÑÑÑÐ²ÑÑÑ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ðº Ð°Ð½Ð°Ð»Ð¸Ð·Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÑÑ ÐºÐ¾Ð½ÐµÑÐ½ÑÐµ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð° Ð¿Ð»ÐµÐ½ÐºÐ¸ Ð¿Ð¾ Ð²ÑÐ¾Ð´Ð½ÑÐ¼ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ°Ð¼ Ð² ÑÑÐ¾Ð»Ñ ÑÐ»Ð¾Ð¶Ð½Ð¾Ð¼ Ð½ÐµÐ»Ð¸Ð½ÐµÐ¹Ð½Ð¾Ð¼ Ð¿ÑÐ¾ÑÐµÑÑÐµ Ñ ÑÐ¾ÑÐ¾ÑÐµÐ¹ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ. ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð¾, ÑÑÐ¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð´Ð¾Ð±Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð² Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ðµ Ð¼Ð¾Ð¶ÐµÑ ÑÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾ ÑÐ¾ÐºÑÐ°ÑÐ¸ÑÑ ÑÐ¸ÑÐ»Ð¾ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð´Ð»Ñ Ð¿Ð¾Ð»Ð½Ð¾Ð³Ð¾ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÐºÐ°ÑÑÑ ÑÐ¾ÑÑÐ¾Ð²ÑÑ ÑÐµÐ¶Ð¸Ð¼Ð¾Ð² Ð² ÑÐ¾Ð¼ Ð¸Ð»Ð¸ Ð¸Ð½Ð¾Ð¼ ÑÐ¾ÑÑÐ¾Ð²Ð¾Ð¼ Ð¿ÑÐ¾ÑÐµÑÑÐµ.
description: The given work is devoted to the collection, processing, and generalization of all arrays of experimental results obtained to date on the synthesis of SiC/Si thin films by the method of atomic substitution, as well as the determination and discovery of hidden dependencies in them, and the compilation of predictive models using machine learning for the further development of the method. For each of the experiments, the properties of the resulting layers were measured, thus obtaining a large data set in which the final properties of the grown film were compared to the input growth parameters. In this work, to approximate and generalize all experimental results, various machine learning approaches are used, including regression using the gradient boosting algorithm, the main idea of which is the combination of weak predictive models obtained during an iterative process, where at each iteration a new model is trained with taking previous ones errors into account. The results obtained indicate that the proposed machine learning approaches to the analysis of experimental results and the developed models make it possible to predict the final properties of the film from the input parameters in such a complex nonlinear process with decent accuracy. It is shown that the use of such methods in the future can significantly reduce the number of necessary experiments to completely construct a map of growth regimes in a particular growth process.