Extended short-wave infrared high-speed all-GeSn PIN photodetectors on silicon
Bibliographic record
Abstract
There is an increasing need for silicon-compatible high-bandwidth extended-short wave infrared (e-SWIR) photodetectors (PDs) to implement cost-effective and scalable optoelectronic devices. These systems are quintessential to address several technological bottlenecks in detection and ranging, surveillance, ultrafast spectroscopy, and imaging. In fact, current e-SWIR high-bandwidth PDs are predominantly made of III–V compound semiconductors and thus are costly and suffer a limited integration on silicon besides a low responsivity at wavelengths exceeding 2.3 μm. To circumvent these challenges, Ge1−xSnx semiconductors have been proposed as building blocks for silicon-integrated high-speed e-SWIR devices. Herein, this study demonstrates vertical all-GeSn PIN PDs consisting of p-Ge0.92Sn0.08/i-Ge0.91Sn0.09/n-Ge0.89Sn0.11 and p-Ge0.91Sn0.09/i-Ge0.88Sn0.12/n-Ge0.87Sn0.13 heterostructures grown on silicon following a step-graded temperature-controlled epitaxy protocol. The performance of these PDs was investigated as a function of the device diameter in the 10–30 μm range. The developed PD devices yield a high bandwidth of 12.4 GHz at a bias of 5 V for a device diameter of 10 μm. Moreover, these devices show a high responsivity of 0.24 A/W, a low noise, and a 2.8 μm cutoff wavelength, thus covering the whole e-SWIR range.
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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".