Imaging through fog using silicon-integrated GeSn PIN extended-SWIR photodetectors
Bibliographic record
Abstract
Optoelectronic devices operating in the extended short-wave infrared (e-SWIR) covering the 1.4-3.0 μm wavelength range provide valuable information that can not be gathered in the visible wavelengths. For instance, e-SWIR penetrates fog, haze, and smog. The current e-SWIR technologies utilize predominantly expensive III-V and II-VI semiconductors, hindering the large-scale use of e-SWIR devices. Herein, we introduce GeSn devices monolithically integrated on Si wafers as a low-cost, scalable, and CMOS-compatible e-SWIR technology. E-SWIR imaging through fog is demonstrated utilizing the grown GeSn PIN photodetectors. To record the images, focused light from a broadband source was directed through a silicon (Si) wafer and an artificial fog toward the GeSn photodetector. Using raster scanning and a single GeSn photodiode epitaxially grown on Si wafer, full images were composed. The latter showed a clear contrast between the illuminated and the dark zones. This capacity to properly detect objects through obscurants opens a range of opportunities for real-life applications in e-SWIR imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".