A review of silicon subwavelength gratings: building break‐through devices with anisotropic metamaterials
Bibliographic record
Abstract
Abstract Silicon photonics is playing a key role in areas as diverse as high‐speed optical communications, neural networks, supercomputing, quantum photonics, and sensing, which demand the development of highly efficient and compact light‐processing devices. The lithographic segmentation of silicon waveguides at the subwavelength scale enables the synthesis of artificial materials that significantly expand the design space in silicon photonics. The optical properties of these metamaterials can be controlled by a judicious design of the subwavelength grating geometry, enhancing the performance of nanostructured devices without jeopardizing ease of fabrication and dense integration. Recently, the anisotropic nature of subwavelength gratings has begun to be exploited, yielding unprecedented capabilities and performance such as ultrabroadband behavior, engineered modal confinement, and sophisticated polarization management. Here we provide a comprehensive review of the field of subwavelength metamaterials and their applications in silicon photonics. We first provide an in‐depth analysis of how the subwavelength geometry synthesizes the metamaterial and give insight into how properties like refractive index or anisotropy can be tailored. The latest applications are then reviewed in detail, with a clear focus on how subwavelength structures improve device performance. Finally, we illustrate the design of two ground‐breaking devices in more detail and discuss the prospects of subwavelength gratings as a tool for the advancement of silicon photonics.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".