An investigation and analysis of plasmonic modulators: a review
Bibliographic record
Abstract
Abstract Plasmonics is an emerging and very advantageous technology which provides high speed and tiny size devices for fulfilling the demand of today’s high-speed world. SPPs are the information carrying elements in plasmonics, which are capable of breaking the diffraction limit. Plasmonics technology has shown its application in uncountable nanophotonic applications like switching, filtering, light modulation, sensing and in many more fields. Modulators are the key components of integrated photonic system. Various modulators which work on different effects are discussed in this study for providing a universal idea of modulators to researchers. Some useful plasmonic active materials are also discussed which are used in most of plasmonic modulators and other active devices. Previously, many researchers have worked on many kinds of modulators and switches, which operate on different kind of operating principles. For providing an overview about plasmonic modulators, their classification and their operation, we have discussed the state of art of some previously introduced modulators and switches which operates on electro-refractive effects and include electro-optic effect, Pockels effect, free charge carrier dispersion effect, phase change effect, elasto-optic effect, magneto-optic effect, and thermo-optic effect. Instead of different effects used in plasmonic switches and modulators different active materials like liquid crystals, graphene, vanadium di-oxide, chalcogenides, polymers, indium tin oxide, bismuth ferrite, barium titanate, and lithium niobate are also explained with their properties. Additionally, we also compared modulators based on different effects in terms of their design characteristics and performances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".