Mid-infrared Imaging Using Strain-Relaxed Ge<sub>1–<i>x</i></sub>Sn<sub><i>x</i></sub> Alloys Grown on 20 nm Ge Nanowires
Bibliographic record
Abstract
Germanium–tin (Ge 1– x Sn x ) semiconductors are a front-runner platform for compact mid-infrared devices due to their tunable narrow bandgap and compatibility with silicon processing. However, their large lattice parameter has been a major hurdle, limiting the quality of epitaxial layers grown on silicon or germanium substrates. Herein, we demonstrate that 20 nm Ge nanowires (NWs) act as effective compliant substrates to grow extended defect-free Ge 1– x Sn x alloys with a composition uniformity over several micrometers along the NW growth axis without significant buildup of the compressive strain. Ge/Ge 1– x Sn x core/shell NWs with Sn content spanning the 6–18 at. % range are achieved and processed into photoconductors exhibiting a high signal-to-noise ratio at room temperature with a cutoff wavelength in the 2.0–3.9 μm range. The processed NW devices are integrated in an uncooled imaging setup enabling the acquisition of high-quality images under both broadband and laser illuminations at 1550 and 2330 nm without the lock-in amplifier technique.
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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".