Lattice Vibrational Modes in Epitaxial Metastable Germanium-Tin Semiconductors
Bibliographic record
Abstract
Group IV semiconductors, such as GeSn and SiGeSn, are of growing interest for silicon-compatible electronic and photonic applications, notably due to the possibility of tuning the bandgap directness and energy to cover a broad range from the short-wave infrared (SWIR) to the mid-infrared (MIR) [1]. While germanium is an indirect-bandgap semiconductor, an indirect-to-direct transition in GeSn alloys is expected for Sn incorporation higher than 9%, but the residual compressive strain typical to epitaxial layers increases the concentration of Sn to obtain a direct-bandgap [2]. Therefore, an understanding of the interplay of strain and composition on the band structure of GeSn semiconductors is of compelling importance in order to precisely control their physical properties. This calls for accurate analysis of the as-grown material properties and its correlation with the measured optical and electrical performance. Raman spectroscopy is a non-destructive method commonly used to assess the role of the effects of composition and strain in shaping the lattice vibrations. The use of a 633 nm excitation enables a clear detection of all Raman modes in GeSn layers independently of their composition, comparatively to the more restricted results associated with the use of 488 nm[3] or 532 nm [4] excitation. This is plausibly attributed to the fact that this wavelength might be close to resonance with the alloy’s E1 gap [5]. There are very few reports on the identification of the Ge-Sn mode [6] and the disorder-activated mode coinciding with the maximum in the one-phonon density of states in Ge [7], but quantitative analyses of the effects of composition and strain on these modes remain conspicuously missing in literature. With this perspective, this work presents a detailed study of Raman vibrational modes in Sn-rich GeSn semiconductors and how their behavior is affected by temperature. The use of samples of various degrees of Sn composition and in-plane compressive strain allows the decoupling of these effects on the Ge-Ge, DA and Ge-Sn vibrational modes, thus providing an effective platform to test the current predictive models. Samples were grown in a chemical vapor deposition (CVD) reactor using monogermane (GeH 4 ) and tin-tetrachloride (SnCl 4 ) precursors [8]. The GeSn multi-layer heterostuctures with a Sn content in the 4-16 at.% range were grown on a Ge virtual substrate (VS) on a Si wafer. Using X-ray diffraction (XRD), reciprocal space mappings (RSM) were performed on all samples to retrieve the Sn composition and the strain. Temperature-dependent Raman measurements were performed from room temperature (300K) and down to 77 K. In GeSn, the asymmetrical profile of the observed Raman modes is due to alloying as substitutional Sn atoms break the translational symmetry and lead to a relaxation of the momentum selection rule [9]. Therefore, the use of exponentially modified gaussian functions (EMG) as a fitting curve can reflect the asymmetrical profile of the different analyzed Raman modes with much better accuracy [10]. The measurement temperature has a clear effect on the fitting parameters, such as the integrated area of the peak , the full width at half maximum (FWHM), the asymmetry, and its position. For example, the Ge-Sn and Ge-Ge peaks increase in wavenumber with decreasing temperature, while the DA peak shows the opposite trend. Ongoing work focuses on establishing the behavior of the characteristics of each mode. Based on these detailed Raman studies, an exhaustive discussion of the influence of temperature, lattice strain and Sn content on GeSn vibrational modes will be presented. Acknowledgements The authors thank J. Bouchard for the technical support, and NSERC Canada (Discovery, SPG, and CRD Grants), Canada Research Chairs, Canada Foundation for Innovation, Mitacs. Reference [1] S. Gupta, et al., J. Appl. Phys. 113, 7 (2013). [2] A. Attiaoui and O. Moutanabbir, J. Appl. Phys. , 116, 6 (2014). [3] R. R. Lieten et al. , ECS J. Solid State Sci. Technol. , 3, 12, pp. P403–P408 (2014). [4] A. Gassenq et al. , Appl. Phys. Lett. , 110, 11, p. 112101 (2017). [5] R. Chen et al. , ECS J. Solid State Sci. Technol. , 2, 4, pp. P138–P145 (2013). [6] D. Zhang et al. , J. Alloys Compd. , 684, pp. 643–648 (2016). [7] V. D’Costa et al. , Solid State Communications , 144, 5-6, pp. 240-244 (2007). [8] S. Assali, J. Nicolas and O. Moutanabbir, Journal of Applied Physics , 125, 2, p. 025304 (2019). [9] P. Parayanthal and F. H. Pollak, Phys. Rev. Lett. , 52, 20, pp. 1822–1825 (1984). [10] É. Bouthillier et al. , “Decoupling the effects of composition and strain on the vibrational modes of GeSn”, https://arxiv.org/abs/1901.00436 , 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".