Dislocation cluster generation behavior in multicrystalline silicon investigated using twin network analysis

We utilized twin network analysis of polycrystalline materials through graph theory to visualize microstructures and examine the behavior of dislocation cluster generation in multicrystalline silicon grown by directional solidification. This approach allows for a rapid and statistical understanding of microstructures and their correlations by representing these features and their changes as network graphs. Our analysis revealed that dislocation clusters are formed at asymmetric Σ27a grain boundaries, which result from a specific twinning process. Gaining this knowledge is expected to assist in identifying grain boundary groups that can minimize the formation of dislocation clusters.

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Publication Details

Journal
Science and Technology of Advanced Materials
Published
2025-05-28
DOI
https://doi.org/10.1080/14686996.2025.2512703
Primary Topic
Microstructure and mechanical properties
Type
article
Field-Weighted Citation Impact
0.00

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article

Dislocation cluster generation behavior in multicrystalline silicon investigated using twin network analysis

Noritaka Usami, Kentaro Kutsukake, Hiroaki Kudo, Takuto Kojima et al.
Science and Technology of Advanced Materials
Microstructure and mechanical properties
article

Dislocation cluster generation behavior in multicrystalline silicon investigated using twin network analysis

Noritaka Usami, Kentaro Kutsukake, Hiroaki Kudo, Takuto Kojima, Kazuma Torii
article en

Abstract

We utilized twin network analysis of polycrystalline materials through graph theory to visualize microstructures and examine the behavior of dislocation cluster generation in multicrystalline silicon grown by directional solidification. This approach allows for a rapid and statistical understanding of microstructures and their correlations by representing these features and their changes as network graphs. Our analysis revealed that dislocation clusters are formed at asymmetric Σ27a grain boundaries, which result from a specific twinning process. Gaining this knowledge is expected to assist in identifying grain boundary groups that can minimize the formation of dislocation clusters.

Science and Technology of Advanced MaterialsVol. 26(1)
Advanced Energy (United States) (US), RIKEN Center for Advanced Intelligence Project (JP), Nagoya University (JP), National Institute of Advanced Industrial Science and Technology (JP)
Core Research for Evolutional Science and Technology
Openalex Percentile: Top 92%
Microstructure and mechanical properties
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