Architecture for Surface-Emitting Lasers with On-Demand Lasing Wavelength by Nanowire Optical Cavities
Bibliographic record
Abstract
Despite the importance and exciting progress of surface-emitting (SE) semiconductor lasers, we have limited choices of lasing wavelength even today. From an application viewpoint, it is desirable to have an architecture that can allow SE lasing in a wide spectral range, based on the need of applications. Herein, we demonstrate a path for SE lasers with lasing wavelength on demand by exploiting III-nitride nanowire optical cavities formed by low-temperature selective area epitaxy (SAE), combined with fine-tuning of substrate patterns and photonic bands. Moreover, in this study, we focus on the device demonstration in the ultraviolet (UV) spectral range, considering the severe lag in developing SE lasers in the UV wavelength range compared to longer wavelengths, e.g., near-infrared (NIR), as well as the potential applications enabled by UV lasers such as solar blind optical wireless communications. Ultralow threshold wavelength-tunable SE UV lasing is achieved by optical pumping. Moreover, SE UV lasing under direct electric current injection is also achieved. This study not only represents an important step in the journey of SE UV laser development but, more importantly, it lays the ground for SE lasers with lasing wavelength on demand, broadly from NIR to UV.
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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".