Toward a Universal Czochralski Growth Model Leveraging Data-Driven Techniques

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00640620" target="_blank" >RIV/67985807:_____/25:00640620 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1002/adts.202501159" target="_blank" >https://doi.org/10.1002/adts.202501159</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/adts.202501159" target="_blank" >10.1002/adts.202501159</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Toward a Universal Czochralski Growth Model Leveraging Data-Driven Techniques

Popis výsledku v původním jazyce

The Czochralski (Cz) method is widely employed for growing crystalline semiconductors from low-vapor-pressure materials. Although furnace designs vary depending on the material, shared hot-zone components, such as crucibles, supports, heaters, insulation, and radiation shields-indicate the potential for a universal Cz furnace model. This study focuses on Cz furnaces that utilize resistance heating. Data-driven techniques including Decision Trees (DT), Symbolic Regression (SR), Artificial Neural Networks (ANN), and Shapley Additive exPlanations (SHAP) are applied to investigate the relationships between furnace design, process parameters, and crystal quality during bulk crystal growth across a range of materials and scales. DT and SR are employed for their interpretability, ANN for its predictive accuracy, and SHAP to enhance model transparency by quantifying feature importance. The analysis explores the correlation between solid–liquid interface deflection, the Voronkov criterion, and 21 input parameters describing furnace geometry, gas composition, crystal and radiation shield thermophysical properties, and growth conditions. The training dataset consists of 632 computational fluid dynamics (CFD) simulations of Cz growth involving silicon, germanium, gallium antimonide, and indium antimonide. Feature engineering using DTs is performed to reduce input dimensionality. The results demonstrate the feasibility of generating a universal Cz growth model that utilizes machine learning techniques to optimize performance across diverse grown materials, furnace configurations, and production scales.

Název v anglickém jazyce

Toward a Universal Czochralski Growth Model Leveraging Data-Driven Techniques

Popis výsledku anglicky

The Czochralski (Cz) method is widely employed for growing crystalline semiconductors from low-vapor-pressure materials. Although furnace designs vary depending on the material, shared hot-zone components, such as crucibles, supports, heaters, insulation, and radiation shields-indicate the potential for a universal Cz furnace model. This study focuses on Cz furnaces that utilize resistance heating. Data-driven techniques including Decision Trees (DT), Symbolic Regression (SR), Artificial Neural Networks (ANN), and Shapley Additive exPlanations (SHAP) are applied to investigate the relationships between furnace design, process parameters, and crystal quality during bulk crystal growth across a range of materials and scales. DT and SR are employed for their interpretability, ANN for its predictive accuracy, and SHAP to enhance model transparency by quantifying feature importance. The analysis explores the correlation between solid–liquid interface deflection, the Voronkov criterion, and 21 input parameters describing furnace geometry, gas composition, crystal and radiation shield thermophysical properties, and growth conditions. The training dataset consists of 632 computational fluid dynamics (CFD) simulations of Cz growth involving silicon, germanium, gallium antimonide, and indium antimonide. Feature engineering using DTs is performed to reduce input dimensionality. The results demonstrate the feasibility of generating a universal Cz growth model that utilizes machine learning techniques to optimize performance across diverse grown materials, furnace configurations, and production scales.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2025

Kód důvěrnosti údajů

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

Název periodika

Advanced Theory and Simulations

ISSN

2513-0390

e-ISSN

2513-0390

Svazek periodika

8

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

17

Strana od-do

e01159

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105016826902