Exceptional points in hybrid-plasmonic quasiparticles for ultracompact modulators
Bibliographic record
Abstract
Current progress in electro-optical modulation within silicon integrated photonics, driven by the unique capabilities of advanced functional materials, has led to significant improvements in device performance. However, inherent constraints in dimensionality and tunability still pose challenges for further innovation. In this work we propose a strategy that exploits the principles of non-Hermitian physics---specifically, the concept of exceptional points (EPs)---to transcend these limitations and pave the way for the next generation of versatile, high-performance photonic devices. Our multilayer structure supports hybrid plasmonic waveguide modes that can manifest as various orders of quasiparticles. By judiciously setting spatial parameters, the system can be tuned to exhibit both weak and strong coupling regimes between the plasmonic and dielectric modes, leading to the controlled formation of EP degeneracies. Furthermore, the integration of low-loss phase-change materials (${\mathrm{Sb}}_{2}{\mathrm{S}}_{3}$ and ${\mathrm{Sb}}_{2}{\mathrm{Se}}_{3}$) enables dynamic electrical tuning, resulting in pronounced modulation of propagation loss and transmission coefficients over submicron distances. This superior performance not only sets a new benchmark for device responsivity and compactness but also opens promising avenues for future research, including the incorporation of gain media for loss compensation at EPs and the exploration of alternative tunable materials for next-generation ultracompact photonic 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".