Binary Super Grating (BSG) Self-Collimated Multi-Wavelength Laser
Bibliographic record
Abstract
This project concerns the exploration of two novel and enabling technologies: a self-collimated multi-wavelength laser (SCMWL). and the underlying binary supergrating (BSG). on which the SCMWL depends. In this context, the BSG functions as a multi-wavelength reflector which can be implemented by a simple two-level etching process. The SCMWL makes use of BSGs to form a planar cavity with collimated multi-wavelength resonances, when combined with gain, the result is self-collimated multi-wavelength output. This project began at the University of Toronto. under grant number DAAG55-98-0435, where it was demonstrated for the first time that both the BSG and the SCMWL concept work, through implementations in an optically-pumped AlGaAs planar waveguide. After Year 1 of this 2-year project, Professor Xu's lab relocated to Brown University, where work on this project has continued. Here we report on this second year of exploration, in which we have extended and solidified our understanding of SCMWL operation, and developed a measurement methodology for these novel devices. This provides a firm foundation for the next phase of SCMWL development, including electrically-pumped implementations promising greater efficiency and output power.
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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".