Theoretical Study of Hybrid Guided Modes in a Multilayer Symmetrical Planar Plasmonic Waveguide
Bibliographic record
Abstract
We presented a comprehensive theoretical study of the hybrid guided mode in a multilayer symmetrical planar plasmonic waveguide, which is constructed with a thin film metal layer symmetrically sandwiched by three dielectric low/high/low-index layers. The seven-layer planar plasmonic structure can support super long-range plasmonic modes with strong subwavelength confinement in the low-index gap layer. We derived the dispersion equations for the guided mode and characterized the hybrid guided mode based on our derived analytical expressions. We explained how the variations in the thickness of the low-index gap and high-index cladding could change the types of the hybrid mode from strong surface plasmon polariton (SPP)-like mode, to SPP-dielectric waveguide (DW)-like mode, and further to strong-DW-like mode. We also found that by tailoring the geometric dimensions of the waveguide, the plasmonic mode of the multilayer structure can be optimized with the strongest mode confinement at the nanoscale gap. The combination of tight light confinement and long-range propagation length makes the seven-layer plasmonic waveguide an excellent candidate for applications in chip-scale plasmonic integrated circuits. The presented theoretical analysis shall be very useful in the design and optimization of active and passive nanoplasmonic devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".