Single-mode surface plasmon distributed feedback lasers
Bibliographic record
Abstract
Single-mode surface plasmon distributed feedback (DFB) lasers are realized in the near infrared using a two-dimensional non-uniform long-range surface plasmon polariton structure. The surface plasmon mode is excited onto a 20 nm-thick, 1 μm-wide metal stripe (Ag or Au) on a silica substrate, where the stripe is stepped in width periodically, forming a 1st order Bragg grating. Optical gain is provided by optically pumping a 450 nm-thick IR-140 doped PMMA layer as the top cladding, which covers the entire length of the Bragg grating, thus creating a DFB laser. Single-mode lasing peaks of very narrow linewidth were observed for Ag and Au DFBs near 882 nm at room temperature. The narrow linewidths are explained by the low spontaneous emission rate into the surface plasmon lasing mode as well as the high quality factor of the DFB structure. The lasing emission is exclusively TM polarized. Kinks in light-light curves accompanied by spectrum narrowing were observed, from which threshold pump power densities can be clearly identified (0.78 MW cm-2 and 1.04 MW cm-2 for Ag and Au DFB lasers, respectively). The Schawlow-Townes linewidth for our Ag and Au DFB lasers is estimated and very narrow linewidths are predicted for the lasers. The lasers are suitable as inexpensive, recyclable and highly coherent sources of surface plasmons, or for integration with other surface plasmon elements of similar structure.
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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) | 0.002 | 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".