Data-Driven Cz–Si Scale-Up under Conditions of Partial Similarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00585427" target="_blank" >RIV/67985807:_____/24:00585427 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1002/crat.202300342" target="_blank" >https://doi.org/10.1002/crat.202300342</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/crat.202300342" target="_blank" >10.1002/crat.202300342</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-Driven Cz–Si Scale-Up under Conditions of Partial Similarity

Popis výsledku v původním jazyce

In Cz–Si growth, the shape of the solid–liquid interface and the v/G ratio significantly impact crystal quality. This study utilizes a data-driven approach, employing multilayer perceptron (MLP) neural networks and Bayesian optimization, to investigate the scale-up process of Cz–Si under conditions of partial similarity. The focus is on exploring the influence of various process and furnace geometry parameters, as well as radiation shield material properties, on the critical measures of crystal quality. Axisymmetric CFD modeling produces 340 sets of 18D raw data, from which 14-dimensionless derived data tuples are generated for the design and training of the MLP. The best MLP obtained demonstrates the ability to accurately assess the complex nonlinear dependencies among dimensionless numbers derived from CFD data and, on the output side, interface deflection and v/G. These relationships, crucial for scale-up, are successfully generalized across a wide range of parameters.

Název v anglickém jazyce

Data-Driven Cz–Si Scale-Up under Conditions of Partial Similarity

Popis výsledku anglicky

In Cz–Si growth, the shape of the solid–liquid interface and the v/G ratio significantly impact crystal quality. This study utilizes a data-driven approach, employing multilayer perceptron (MLP) neural networks and Bayesian optimization, to investigate the scale-up process of Cz–Si under conditions of partial similarity. The focus is on exploring the influence of various process and furnace geometry parameters, as well as radiation shield material properties, on the critical measures of crystal quality. Axisymmetric CFD modeling produces 340 sets of 18D raw data, from which 14-dimensionless derived data tuples are generated for the design and training of the MLP. The best MLP obtained demonstrates the ability to accurately assess the complex nonlinear dependencies among dimensionless numbers derived from CFD data and, on the output side, interface deflection and v/G. These relationships, crucial for scale-up, are successfully generalized across a wide range of parameters.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Crystal Research and Technology

ISSN

0232-1300

e-ISSN

1521-4079

Svazek periodika

59

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

10

Strana od-do

2300342

Kód UT WoS článku

001198587000001

EID výsledku v databázi Scopus

2-s2.0-85189762600