Broad-Band Antireflection Coatings for Improved Grating-External-Cavity Diode Laser Performance
Bibliographic record
Abstract
In this thesis, strong optical feedback is utilized to realize broad-band wavelength tuning and to stabilize the frequency of a semiconductor diode laser in a grating-external-cavity (GEC) configuration. To reach the regime of strong optical feedback, the laser facet through which the feedback occurs has to be antireflection (AR) coated. Multi-layer AR coatings were designed using SiO2, Si3N4, SiOxNy, and a:Si for specific laser waveguide structures, and were fabricated by an electron cyclotron resonance, plasma enhanced, chemical vapor deposition (ECR-PECVD) system. The film thickness and refractive index were monitored by in situ ellipsometry during the deposition. This scheme permitted very low reflectivities, in the order of 5 x 10-4, to be readily and reproducibly obtained. The diode laser thus obtained was used in a strong feedback configuration. Light emitted from the coated facet was collimated and fed back onto the laser cavity after being reflected off a diffraction grating. The diffraction grating provides frequency selectivity, which is a desirable feature for obtaining a stable single longitudinal mode laser. The laser in this configuration oscillated in a single mode with a greater than 30 dB side mode suppression ratio and a wide tuning 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".