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Automatic etch pit density analysis in multicrystalline silicon

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2020

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Open Access-Veröffentlichung
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Published

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Computational Materials Science. Elsevier. 2020, 183, 109886. ISSN 0927-0256. eISSN 1879-0801. Available under: doi: 10.1016/j.commatsci.2020.109886

Zusammenfassung

This publication contains a description and is published in combination with the source code for a tool capable of determining the etch pit density (EPD) on multicrystalline silicon image data. The algorithm is capable of classifying grain boundaries and polishing scratches and removes these structures from the analysis result. Included with the analysis code are methods for plotting EPD maps as well as relative EPD frequency. This is combined with a brief description of the experimental steps of wafer preparation and defect etching as well as a discussion of the analysis limitations.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
530 Physik

Schlagwörter

Defect etching, Etch pit density, Dislocation density, Crystal defects, Multicrystalline silicon, mc-Si

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ISO 690FLECK, Martin, Giso HAHN, 2020. Automatic etch pit density analysis in multicrystalline silicon. In: Computational Materials Science. Elsevier. 2020, 183, 109886. ISSN 0927-0256. eISSN 1879-0801. Available under: doi: 10.1016/j.commatsci.2020.109886
BibTex
@article{Fleck2020-10Autom-50334,
  year={2020},
  doi={10.1016/j.commatsci.2020.109886},
  title={Automatic etch pit density analysis in multicrystalline silicon},
  volume={183},
  issn={0927-0256},
  journal={Computational Materials Science},
  author={Fleck, Martin and Hahn, Giso},
  note={Erratum: https://dx.doi.org/10.1016/j.commatsci.2020.110200 Article Number: 109886}
}
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Interner Vermerk

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Erratum: https://dx.doi.org/10.1016/j.commatsci.2020.110200
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