Waveguide-coupled plasmonic nanogap-integrated phase change metasurfaces
Bibliographic record
Abstract
Silicon photonics has emerged as the dominant technology platform for short distance, inter-chip communication for a variety of photonic computing and sensing applications due to its efficiency in modulation and confinement of light across telecom frequencies in addition to its inherent CMOS compatibility. The integration of metallic nanogaps within silicon photonic architectures provides a promising route for scaling this platform through the extreme confinement offered by plasmonics while providing an efficient route to interfacing future photonic integrated circuits with electronics. However, fabricating the gap sizes (< λg/10) required of plasmonic resonating nanogaps for efficient operation across telecommunication frequencies is highly challenging. Efficient coupling from waveguides to plasmonic nanogaps also remains a major source of loss. Here, we show that the key to merging these platforms lies in applying metamaterial/metasurface engineering principles to the design of the nanogap. Over the last decade, metamaterials and metasurfaces have emerged as a versatile toolkit for control and enhancement of light-matter interaction at application-driven wavelengths of interest in nanophotonic device platforms. We show that integrating a metagrating within a waveguide-coupled plasmonic nanogap made from Au, can enhance coupling to and from the silicon waveguides. Furthermore, the incorporation of the metasurface within the gap allows resonant response to be maintained at user-specified wavelength of interest with gaps as large as λg/5, drastically easing fabrication. Finally, we show that by incorporating a reconfigurable phase change chalcogenide alloy into the gap, non-volatile signal switching with modulation contrasts of up to 10:1 can be achieved across telecom frequencies.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".