Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy
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arXiv
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| Format: | Preprint |
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2023
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| author | Gunder, Calbi Alavijeh, Mohammad Zamani Wangila, Emmanuel de Oliveira, Fernando Maia Sheibani, Aida Kryvyi, Serhii Attwood, Paul C. Mazur, Yuriy I. Yu, Shui-Qing Salamo, Gregory J. |
| author_facet | Gunder, Calbi Alavijeh, Mohammad Zamani Wangila, Emmanuel de Oliveira, Fernando Maia Sheibani, Aida Kryvyi, Serhii Attwood, Paul C. Mazur, Yuriy I. Yu, Shui-Qing Salamo, Gregory J. |
| contents | The growth of high-composition GeSn films of the future will likely be guided via algorithms. In this study we show how a logarithmic-based algorithm can be used to obtain high-quality GeSn compositions up to 16 % on GaAs (001) substrates via molecular beam epitaxy. Within we demonstrate composition targeting and logarithmic gradients to achieve linearly graded pseudomorph Ge1-xSnx compositions up to 10 % before partial relaxation of the structure and a continued gradient up to 16 % GeSn. In this report, we use X-ray diffraction, simulation, SIMS and atomic force microscopy to analyze and demonstrate some of the possible growths that can be produced with the enclosed algorithm. This methodology of growth is a major step forward in the field of GeSn development and the first demonstration of algorithmically driven, linearly graded GeSn films. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06695 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy Gunder, Calbi Alavijeh, Mohammad Zamani Wangila, Emmanuel de Oliveira, Fernando Maia Sheibani, Aida Kryvyi, Serhii Attwood, Paul C. Mazur, Yuriy I. Yu, Shui-Qing Salamo, Gregory J. Materials Science Applied Physics The growth of high-composition GeSn films of the future will likely be guided via algorithms. In this study we show how a logarithmic-based algorithm can be used to obtain high-quality GeSn compositions up to 16 % on GaAs (001) substrates via molecular beam epitaxy. Within we demonstrate composition targeting and logarithmic gradients to achieve linearly graded pseudomorph Ge1-xSnx compositions up to 10 % before partial relaxation of the structure and a continued gradient up to 16 % GeSn. In this report, we use X-ray diffraction, simulation, SIMS and atomic force microscopy to analyze and demonstrate some of the possible growths that can be produced with the enclosed algorithm. This methodology of growth is a major step forward in the field of GeSn development and the first demonstration of algorithmically driven, linearly graded GeSn films. |
| title | Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy |
| topic | Materials Science Applied Physics |
| url | https://arxiv.org/abs/2309.06695 |