Dispersion-engineered phase change material integrated silicon photonics modulators with controlled insertion losses
Bibliographic record
Abstract
Photonic modulators have seen widespread use in optical circuitry, optical processing, and next-generational computing regimes such as neuromorphic computing. Prior research has focused on the incorporation of high-index functional materials on or adjacent to photonic circuit components such as modulators to enhance signal detection, modulation, and generation. The reversible, non-volatile transitions between optically and electrically unique amorphous and crystalline material phases inherent to chalcogenide phase-change materials (PCMs) present a promising material platform for this integration. However, current methods of incorporation combined with lossy material properties lead to integrations having large insertion losses and device footprints. Here we demonstrate that applying metamaterial effective medium theory enables dispersive engineering to drastically reduce insertion losses and footprints in PCM-loaded optical circuitry. Two configurations are explored, a metagrating and a multilayer, in which full-π modulator phase shifts are achieved in compact footprints down to 4.36 and 4.7 𝜇m with low insertion losses at a 1550nm wavelength.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".