Self-biased broadband magnet-free linear isolator based on one-way space-time coherency
Bibliographic record
Abstract
This paper introduces a self-biased broadband magnet-free and linear isolator based on one-way space-time coherency. The incident wave and the space-time-modulated medium share the same temporal frequency and are hence temporally coherent. However, thanks to the unidirectionally of the space-time modulation, the space-time-modulated medium and the incident wave are spatially coherent only in the forward direction and not in the opposite direction. As a consequence, the energy of the medium strongly couples to the propagating wave in the forward direction, while it conflicts with the propagating wave in the opposite direction, yielding strong isolation. We first derive a closed-form solution for the wave scattering from a spatiotemporally coherent medium and then show that a perfectly coherent space-time-modulated medium provides a moderate isolation level which is also subject to one-way transmission gain. To overcome this issue, we next investigate the effect of space-coherency imperfection between the medium and the wave, while they are still perfectly temporally coherent. Leveraging the spatial-coherency imperfection, the medium exhibits a quasiarbitrary and strong nonreciprocal transmission. Finally, we present the experimental demonstration of the self-biased version of the proposed broadband isolator, exhibiting more than $122%$ fractional operation bandwidth.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".