Magnetoplasmonic Faraday Rotators: Enabling Gigahertz Active Polarization Control for Integrated Plasmonics
Bibliographic record
Abstract
We present the design and theoretical characterization of a magnetoplasmonic Faraday rotator for active polarization control in integrated plasmonics. By incorporating bismuth-substituted yttrium iron garnet (Bi:YIG) into a unique hybrid ridge–plasmonic waveguide structure, we effectively overcome the phase-matching limitations between photonic TE and plasmonic TM modes, and hence attain efficient Faraday rotation within a plasmonic device. The device provides 99.4% polarization conversion within a length of 830 μm, while the two modes exhibit propagation lengths in excess of 1 mm. This versatile optical building block can be operated with either a TE or TM input, making it ideal for polarization switching and polarization division multiplexing. Additionally, a buried Ag transmission line under the waveguide facilitates high-speed active polarization modulation by generating transient magnetic fields and modulating the Bi:YIG magnetization. We show that with these transient fields and an external static biasing field one can operate the device in either a pulsed-input pulsed-output mode, to produce a polarization switch, or a pulsed-input continuous-output mode, to produce a polarization oscillator. Such a device is shown to be capable of polarization modulation of 10 GHz and will be vital in realizing plasmonic circuits employing polarization diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".