Recent advances in metamaterial integrated photonics
Bibliographic record
Abstract
Since the invention of the silicon subwavelength grating waveguide in 2006, subwavelength metamaterial engineering has become an essential design tool in silicon photonics. Employing well-established nanometer-scale semiconductor manufacturing techniques to create metamaterials in optical waveguides has allowed unprecedented control of the flow of light in photonic chips. This is achieved through fine-tuning of fundamental optical properties such as modal confinement, effective index, dispersion, and anisotropy, directly by lithographic imprinting of a specific subwavelength grating structure onto a nanophotonic waveguide. In parallel, low-loss mode propagation is readily obtained over a broad spectral range since the subwavelength periodicity effectively avoids losses due to spurious resonances and bandgap effects. In this review we present recent advances achieved in the surging field of metamaterial integrated photonics. After briefly introducing the fundamental concepts governing the propagation of light in periodic waveguides via Floquet–Bloch modes, we review progress in the main application areas of subwavelength nanostructures in silicon photonics, presenting the most representative devices. We specifically focus on off-chip coupling interfaces, polarization management and anisotropy engineering, spectral filtering and wavelength multiplexing, evanescent field biochemical sensing, mid-infrared photonics, and nonlinear waveguide optics and optomechanics. We also introduce a nascent research area of resonant integrated photonics leveraging Mie resonances in dielectrics for on-chip guiding of optical waves, with the first Huygens’ metawaveguide recently demonstrated. Finally, we provide a brief overview of inverse design approaches and machine-learning algorithms for on-chip optical metamaterials. In our conclusions, we summarize the key developments while highlighting the challenges and future prospects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".