Unraveling conditions for W-shaped interface and undercooled melts in Cz-Si growth: A smart approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00600768" target="_blank" >RIV/67985807:_____/24:00600768 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.jcrysgro.2024.127897" target="_blank" >https://doi.org/10.1016/j.jcrysgro.2024.127897</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jcrysgro.2024.127897" target="_blank" >10.1016/j.jcrysgro.2024.127897</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unraveling conditions for W-shaped interface and undercooled melts in Cz-Si growth: A smart approach

Popis výsledku v původním jazyce

In Cz-Si growth, concave and W-shaped solid–liquid interfaces and undercooled melts are primary contributors to the degradation of crystal quality, particularly structure loss, defect generation, non-uniform dopant distribution, and crystal twisting, making their avoidance crucial. We employed a classification tree machine learning approach to investigate the importance of 15 process and furnace design parameters and their critical ranges for the formation of various types of W-shaped interfaces and undercooled melts at different scales, both in dimensional and dimensionless forms, and across a wide range of process conditions. Moreover, symbolic regression was used to predict minimal melt temperature based on the aforementioned inputs. Training data were obtained by CFD modeling. The classification tree for combined output identified the Grashof, Reynolds for crystal, and Stefan numbers, along with the percentage of silicon solidified, as the most decisive inputs. Symbolic regression for the temperature of undercooled melt highlighted crucible diameter, pulling rate, and the power of the bottom heater as key parameters.

Název v anglickém jazyce

Unraveling conditions for W-shaped interface and undercooled melts in Cz-Si growth: A smart approach

Popis výsledku anglicky

In Cz-Si growth, concave and W-shaped solid–liquid interfaces and undercooled melts are primary contributors to the degradation of crystal quality, particularly structure loss, defect generation, non-uniform dopant distribution, and crystal twisting, making their avoidance crucial. We employed a classification tree machine learning approach to investigate the importance of 15 process and furnace design parameters and their critical ranges for the formation of various types of W-shaped interfaces and undercooled melts at different scales, both in dimensional and dimensionless forms, and across a wide range of process conditions. Moreover, symbolic regression was used to predict minimal melt temperature based on the aforementioned inputs. Training data were obtained by CFD modeling. The classification tree for combined output identified the Grashof, Reynolds for crystal, and Stefan numbers, along with the percentage of silicon solidified, as the most decisive inputs. Symbolic regression for the temperature of undercooled melt highlighted crucible diameter, pulling rate, and the power of the bottom heater as key 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

Journal of Crystal Growth

ISSN

0022-0248

e-ISSN

1873-5002

Svazek periodika

648

Číslo periodika v rámci svazku

December 2024

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

127897

Kód UT WoS článku

001322235000001

EID výsledku v databázi Scopus

2-s2.0-85204676333