Tunable Hybrid Plasmonic Semiconductor Laser Based on Loss Perturbation
Bibliographic record
Abstract
We propose a tunable plasmonic semiconductor laser that exploits loss perturbation as a tuning mechanism. A metal oxide semiconductor (MOS) capacitive structure is added on top of an edge-emitting Fabry-Perot (FP) diode laser, such that a hybrid plasmonic TM mode that overlaps partly with the MOS capacitor and the semiconductor gain region is supported as the lasing mode. We also propose the use of a layer of conductive oxide, e.g., indium tin oxide (ITO), as the semiconductor of the MOS structure, because the epsilon near zero (ENZ) condition can be attained therein under accumulation, thereby producing a very large change in the effective index of the hybrid plasmonic TM mode. The change in the imaginary part of the effective index is used to tune the lasing wavelength - exploiting loss perturbation to achieve laser tuning is paradigm-shifting. The laser proposed operates at telecom wavelengths, requiring an electrical forward bias to pump the active layer, and a gate voltage to drive the MOS tuning capacitor. Simulations yield a tuning range of over 7 nm in the O-band for a$100 \mu \text{m}$long FP laser cavity.
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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 | 0.000 |
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".