Integrated Photonic Modulators Using Dispersion Engineered Phase Change Metasurfaces
Bibliographic record
Abstract
With the advancement and availability of high-resolution lithography techniques, nanostructuring high-index dielectric and plasmonic media on the subwavelength scale have demonstrated high-quality factor optical resonant devices within the umbrella of metamaterials and metasurfaces [1]–[3]. In this realm, subwavelength nanostructuring can also offer non-resonant dispersion engineering of a given dielectric or plasmonic material with finite geometry. Concurrently, silicon photonics has become a key technology platform for the field of telecommunications and optical computing, and significant research has focused on the incorporation of a wide range of high-index functional materials including germanium, 2D materials such as graphene, and lithium niobate on or adjacent to waveguide structures for signal detection, modulation, and generation. Among these material choices, chalcogenide phase-change materials are being extensively studied for emerging silicon photonics architectures in neuromorphic computing and telecommunication networks [4]. Chalcogenide phase-change alloys, comprised of group 16 elements such as sulphur, selenium and tellurium, are high refractive index media in the infrared spectrum exhibiting a unique reversible, non-volatile switching between the amorphous and crystalline material phases. Transitioning between phases can be actuated through optical, electrical, or thermal stimuli; thus, making them ideal for integration with silicon photonics. However, the mitigation of high insertion losses when introducing these materials to the vicinity of the waveguide due to inherent optical losses, have resulted in devices with large footprints and poor modulation contrasts. This is a real barrier to scaling up of such systems to the number of nodes needed per chip for efficient realistic neuromorphic acceleration and processing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".