High-speed nonreciprocal magnetoplasmonic waveguide phase shifter
Bibliographic record
Abstract
In the realm of modern day optical computing prospects, plasmonics provides a unique means of consolidating electronics and photonics in a nanoscale platform. For advanced optical information processing applications, there is a pressing need for configurable plasmonic platforms that mimic the functionalities of their electrical counterparts. In such a pursuit, the ability to actively manipulate plasmonic phenomena must first be realized via an electrical stimulus. An attractive architecture is realized by incorporating magneto-optical materials into devices, as the degree of electrical control they provide is unattainable by other material systems. Properties of the material are influenced by external magnetic fields, which can be conveniently generated by current passing through nearby metallic structures. While ferromagnetic metals are far too lossy for plasmonic integration, magnetic garnets can facilitate a robust and tunable architecture in a waveguide geometry. Here, we investigate a bismuth-substituted yttrium iron garnet platform for a high bandwidth active optical phase shifter. Our device is capable of imparting a large nonreciprocal phase shift of 6.99 rad/mm, while confining 70% of the modal power to the 0.081 μm2 cross-sectional area of the core. By considering the Landau–Lifshitz–Gilbert formalism, we show that the magnetoplasmonic phase shifter is operable in both underdamped and critically damped modes, and is fully tunable through the applied magnetic fields and pulsewidth. This magnetoplasmonic building block opens doors to a new class of nanoplasmonic devices, such as optical phase modulators, isolators, and optical clocks that will satisfy key applications in nanoscale optical information networks.
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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 | 0.001 | 0.000 |
| 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".