Mid-Infrared Detectors and Imagers Integrating All-Group IV Nanowires
Bibliographic record
Abstract
Cost-effective mid-wave infrared (MWIR) optoelectronic devices are of utmost importance to a plethora of applications such as night vision, thermal sensing, autonomous vehicles, free-space communication, and spectroscopy. To this end, leveraging the ubiquitous silicon-based processing has emerged as a powerful strategy that can be accomplished through the use of group IV germanium-tin (GeSn) alloys. Indeed, due to their compatibility with silicon and their tunable bandgap energy covering the entire MWIR range, GeSn semiconductors are frontrunner platforms for compact and scalable MWIR technologies. However, the GeSn large lattice parameter has been a major hurdle limiting the quality of GeSn epitaxy on silicon wafers. Herein, it is shown that sub-20 nm Ge nanowires (NWs) provide effective compliant substrates to grow Ge$_{1-x}$Sn$_{x}$ alloys with a composition uniformity over several micrometers with a very limited build-up of the compressive strain. Ge/Ge$_{1-x}$Sn$_{x}$ core/shell NWs with Sn content spanning the 6 to 18 at.$\%$ range are demonstrated and integrated in photoconductive devices exhibiting a high signal-to-noise ratio at room temperature and a tunable cutoff wavelength covering the 2.0 $μ$m to 3.9 $μ$m range. Additionally, the processed NW-based detectors were used in uncooled imagers enabling the acquisition of high-quality images under both broadband and laser illuminations without a lock-in 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".