Combined Iodine- and Sulfur-Based Treatments for an Effective Passivation of GeSn Surface
Bibliographic record
Abstract
GeSn alloys are metastable semiconductors that have been proposed as building blocks for silicon-integrated shortwave and midwave infrared photonic and sensing platforms. Exploiting these semiconductors requires, however, the control of their epitaxy and their surface chemistry to reduce nonradiative recombination that hinders the efficiency of optoelectronic devices. Herein, we demonstrate that combined sulfur- and iodine-based treatments yield effective passivation of Ge and Ge 0.9 Sn 0.1 surfaces. X-ray photoemission spectroscopy and in situ spectroscopic ellipsometry measurements were used to investigate the dynamics of surface stability and track the reoxidation mechanisms. Our analysis shows the largest reduction in oxide after HI treatment, while HF + (NH 4 ) 2 S results in a lower reoxidation rate. A combined HI + (NH 4 ) 2 S treatment preserves the lowest oxide ratio of <10% up to 1 h of air exposure, while less than half of the initial oxide coverage is reached after 4 h. These results highlight the potential of S- and I-based treatments in stabilizing the GeSn surface chemistry, thus enabling a passivation method that is compatible with materials and device processing.
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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".