Toward a Universal Czochralski Growth Model Leveraging Data-Driven Techniques
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Toward a Universal Czochralski Growth Model Leveraging Data-Driven Techniques
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advanced Theory and Simulations
ISSN
2513-0390
e-ISSN
2513-0390
Volume of the periodical
8
Issue of the periodical within the volume
12
Country of publishing house
DE - GERMANY
Number of pages
17
Pages from-to
e01159
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105016826902