title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÐºÐ¾ÑÑÐµÐºÑÐ¸Ð¸ ÑÑÐ¾Ð½ÑÐ° ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿ÑÐ¸ ÑÐ¸ÑÐ»ÐµÐ½Ð½Ð¾Ð¼ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸ ÑÐµÐ¿Ð»Ð¾Ð¾Ð±Ð¼ÐµÐ½Ð° Ð² ÑÑÑÐ°Ð½Ð¾Ð²ÐºÐ°Ñ Ð¼ÐµÑÐ¾Ð´Ð° Ð§Ð¾Ñ ÑÐ°Ð»ÑÑÐºÐ¾Ð³Ð¾
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Application of a neural network for a correction of the crystallization front in numerical simulations of heat transfer in Czochralski setups
Creators: name: ÐÑÐµÐ², ÐÐ½Ð´ÑÐµÐ¹
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: 2023
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÑÐ¸ÑÑÐ°Ð»Ð»Ñ
Ð¢ÐµÐ¿Ð»Ð¾Ð¾Ð±Ð¼ÐµÐ½
Ð¼ÐµÑÐ¾Ð´ Ð§Ð¾Ñ ÑÐ°Ð»ÑÑÐºÐ¾Ð³Ð¾
ÑÑÐ¾Ð½Ñ ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸
Czhochralski method
crystallization front

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description: The work investigates the possibility of using of artificial neural networks (ANN) to improve the quality of the results of numerical simulations of heat transfer and crystallization in setups for growing silicon crystals on the base of the Czochralski method. The aim of the study is to improve the modeling of the crystallization front geometry using machine learning technologies. Tasks that were solved in the course of the study: 1. Numerical simulation of flow and heat transfer in the crucible of the Czochralski growth setup in order to obtain the geometry of crystallization fronts for ANN training. 2. Numerical simulation of global heat transfer in industrial Czochralski setups with fixed crystallization front to obtain distributions of additional heat fluxes. 3. Development of a program code and tuning of ANN parameters for predicting distributions of additional heat fluxes. 4. Numerical simulation of global heat transfer in Czochralski setups using additional heat fluxes in order to study the possibilities of the proposed method for improving the modeling of the geometry of the shape of the crystallization front. Computations of the process of growing silicon crystals by the Czochralski method were performed using the CGSim CFD package using two software modules for modeling in steady axisymmetric formulation based on the RANS method of turbulent flow in the region of a silicon melt and a crystal (Flow Module), as well as global heat transfer in the Czochralski method with fixed crystallization front (basic CGSim). The comparison of the crystallization fronts calculated by the Flow Module package with the fronts obtained using the basic CGSim module using the additional heat flux predicted by the ANN showed an improvement in the simulation results relative to the use of the turbulence model built into the basic CGSim module.