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arXiv · 2309.06695

Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy

Calbi Gunder·Mohammad Zamani Alavijeh·Emmanuel Wangila·Fernando Maia de Oliveira·Aida Sheibani·Serhii Kryvyi·Paul C. Attwood·Yuriy I. Mazur·Shui-Qing Yu·Gregory J. Salamo
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Abstract

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.

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Calbi Gunder, Mohammad Zamani Alavijeh, Emmanuel Wangila, Fernando Maia de Oliveira, Aida Sheibani, Serhii Kryvyi, Paul C. Attwood, Yuriy I. Mazur, Shui-Qing Yu, Gregory J. Salamo. 2023-09-13. Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy. https://doi.org/10.3390/nano14110909

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