Enhanced GeSn Microdisk Lasers Directly Released on Si
Bibliographic record
Abstract
Abstract GeSn alloys are promising candidates for complementary metal‐oxide‐semiconductor‐compatible, tunable lasers. Relaxation of residual compressive strain in epitaxial GeSn has recently shown promise in improving the lasing performance. However, the suspended device configuration that is thus far introduced to relax the strain is destined to limit heat dissipation, thus hindering the device performance. Herein is demonstrated that strain‐free GeSn microdisk laser devices fully released on Si outperform the canonical suspended devices. This approach allows to simultaneously relax the limiting compressive strain while offering excellent thermal conduction. Optical simulations confirm that, despite a relatively small refractive index contrast between GeSn and Si, optical confinement in strain‐free GeSn optical cavities on Si is superior to that in conventional strain‐free GeSn cavities suspended in the air. Moreover, thermal simulations indicate a negligible temperature increase in the device. Conversely, the temperature in the suspended devices increases substantially reaching, for instance, 120 K at a base temperature of 75 K under the employed optical pumping conditions. Such improvements enable increasing the operation temperature by ≈40 K and reducing the lasing threshold by 30%. This approach lays the groundwork to implement new designs in the quest for room temperature GeSn lasers on Si.
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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".