Year: 2025
Type: article
Abstract: The Czochralski (Cz) method is a widely used process for growing high‐quality single crystals, critical for applications in semiconductors, optics, and advanced materials. Achieving optimal growth conditions requires precise control of process and furnace design parameters. Still, data scarcity – especially for new materials – limits the application of machine learning (ML) in predictive modeling and optimization. This study proposes a transfer learning approach to overcome this limitation by adapting ML models trained on a higher data volume of one source material (Si) to a lower data volume of another target material (Ge and GaAs). The materials are deliberately selected to assess the robustness of the transfer learning approach in handling varying data similarity, with Cz‐Ge being similar to Cz‐Si, and GaAs grown via the liquid encapsulated Czochralski method (LEC), which differs from Cz‐Si. Various transfer learning strategies are explored, including Warm Start, Merged Training, and Hyperparameters Transfer, and evaluate multiple ML architectures across two different materials. The results demonstrate that transfer learning significantly enhances predictive accuracy with minimal data, providing a practical framework for optimizing Cz growth parameters across diverse materials.
Source: Advanced Theory and Simulations
Institutions: Leibniz Institute for Crystal Growth, Zuse Institute Berlin
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,430
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4412507976