Decoupling the effects of composition and strain on the vibrational modes of GeSn semiconductors
Bibliographic record
Abstract
Abstract We report on the behavior of Ge-Ge, Ge-Sn, Sn-Sn like, and disorder-activated (DA) vibrational modes in GeSn semiconductors investigated using Raman scattering spectroscopy. By using an excitation wavelength close to the E 1 gap, all modes are clearly resolved and their evolution as a function of strain and Sn content is established. Previous Raman scattering studies mainly focused on the Ge-Ge peak position which is insufficient to evaluate the effects of lattice strain and Sn content. Herein to decouple the individual contributions of content and strain, the analysis was conducted on a series of pseudomorphic and relaxed epitaxial layers with a Sn content in the 5–17 at.% range. The frequencies of all vibrational modes were found to display qualitatively the same behavior as a function of content and strain, that is a linear downshift as the Sn content increases or the compressive strain relaxes. Simultaneously, the Ge-Sn and Ge-Ge peaks broaden, and the latter becomes increasingly asymmetric. The behavior of the integrated intensity, width, and asymmetry of each one of these vibrational modes was also evaluated. We found that an increase in Sn content is associated with an increase in the relative integrated intensity of Ge-Sn and DA modes. The latter also increases as the layers become more compressively strained and become more prominent under the x z z x ˉ configuration as the intensity of the adjacent longitudinal optical modes decreases. The Raman mode asymmetry, coupled with the peak position, is exploited to implement an empirical approach to accurately quantify the Sn composition and lattice strain from Raman spectra.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".