Asymmetric long-range hybrid-plasmonic modes in asymmetric nanometer-scale structures
Bibliographic record
Abstract
The structural symmetry required for long-range surface-plasmon-polariton modes to take place is examined and mapped to asymmetric plasmonic structures. This study leads to a design methodology that facilitates the realization through systematic design of long-range modes in any asymmetric hybrid plasmonic waveguide (AHPW). Examining the modal behavior of an AHPW reveals that field symmetry on either side of the metal is the only necessary condition for plasmonic structures to support long-range propagation. We report that this field symmetry condition can be satisfied irrespective of asymmetry in a waveguide structure, material, or even field profile. The structure is analyzed using the coupled mode theory, transfer matrix method, and finite-difference time-domain method. The AHPW supports high-loss antisymmetric and long-range symmetric supermodes. Dispersion of these supermodes with respect to waveguide dimensions display similar anticrossing characteristics to those obtained in two coupled harmonic oscillators, where the propagation losses display peaks and troughs in the vicinity of the anticrossing region. To place the work in perspective, an AHPW with a width of 200 nm was found to support a long-range supermode with a subwavelength mode area of 0.23 μm2 and propagation loss of 0.025 dB·μm−1 at the wavelength of 1550 nm, providing a radically improved attenuation confinement trade-off compared with other common types of plasmonic waveguides.
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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) | 0.000 | 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".