Waveguide-Coupled Mid-Infrared GeSn Membrane Photodetectors on Silicon-on-Insulator
Bibliographic record
Abstract
After thriving in telecommunications over recent decades, silicon photonics is now being extended into the mid-infrared range, where it has the potential to unlock a wide range of valuable opportunities in sensing, imaging, and free-space communications. In view of this perspective, the germanium-tin (GeSn) alloy has been extensively investigated as a silicon-compatible semiconductor with bandgap tunability that covers this entire spectral range. Indeed, a variety of GeSn-based high-performance optoelectronic devices have been demonstrated, confirming the potential of this system for mid-infrared applications. However, the integration of these devices onto silicon photonic platforms remains underexplored. Herein, we demonstrate the fabrication and integration, through transfer-printing, of strain-relaxed GeSn membranes onto silicon-on-insulator waveguides to create integrated detectors operating at wavelengths up to 3.1 μm at room temperature. Two different designs of waveguide structures are evaluated to study the coupling efficiency between the passive structures and the active membrane detector. A responsivity reaching 0.36 A/W at an operating wavelength of 2.33 μm is measured under a bias of 1 V. Moreover, the fabrication resulted in multiple working devices exhibiting similar performance using a single transfer printing step, demonstrating the scalability of the proposed approach.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".