Subwavelength-Grating Metamaterial Structures for Silicon Photonic Devices
Bibliographic record
Abstract
Segmenting silicon waveguides at the subwavelength scale produce an equivalent homogenous material. The geometry of the waveguide segments provides precise control over modal confinement, effective index, dispersion and birefringence, thereby opening up new approaches to design devices with unprecedented performance. Indeed, with ever-improving lithographic technologies offering sub-100-nm patterning resolution in the silicon photonics platform, many practical devices based on subwavelength structures have been demonstrated in recent years. Subwavelength engineering has thus become an integral design tool in silicon photonics, and both fundamental understanding and novel applications are advancing rapidly. Here, we provide a comprehensive review of the state of the art in this field. We first cover the basics of subwavelength structures, and discuss substrate leakage, fabrication jitter, reduced backscatter, and engineering of material anisotropy. We then review recent applications including broadband waveguide couplers, high-sensitivity evanescent field sensors, low-loss devices for mid-infrared photonics, polarization management structures, spectral filters, and highly efficient fiber-to-chip couplers. We finally discuss the future prospects for subwavelength silicon structures and their impact on advanced device design.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".