Asymmetric transmission in nanophotonics
Bibliographic record
Abstract
In a reciprocal medium, transmission of electromagnetic (EM) waves is symmetric along opposite directions which restrict design and implementation of various systems in optics and photonics. Asymmetric transmission (AT) is essential for designing isolators and circulators in optics and photonics, and it benefits other applications such as photovoltaic systems, lasers, cloaking, and EM shielding. While bulky nonreciprocal devices based on magnetic field biases have been well known, creating AT in subwavelength structures is more challenging, and structures with a subwavelength thickness that show AT have drawn a lot of attention over the last decade. Various approaches have been reported to create metasurfaces featuring nonreciprocal transmission, such as plasmonic and dielectric metasurfaces that enhance Faraday rotation, nonlinear metasurfaces with intensity-dependent refractive indices, and implementing spatiotemporal modulation in a metasurface. On the other hand, AT has also been reported in reciprocal structures by creating multiple paths for the transmission of EM waves by changing the polarization of light or redirecting light to higher-order diffraction orders. Here, we present a review of various approaches implemented for realizing AT in subwavelength structures in both reciprocal and nonreciprocal systems. We also discuss the main design principles and limitations of AT achieved in various approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".