Engineering silicon nanostructures for the optimization of nonlinear and optomechanical phenomena in integrated devices
Bibliographic record
Abstract
Subwavelength silicon nanostructures provide unprecedented flexibility in the control of optomechanical and nonlinear effects. In this invited presentation, we will show our most recent results on the use of nanostructures for the optimization of Kerr nonlinearities silicon. We will also discuss our recent advances in the use of subwavelength silicon nanostructures to engineer photons and phonons in suspended and non-suspended optomechanical cavities.Brillouin scattering has garnered a significant interest in various applications in communications, sensing and quantum technologies. Essentially, Brillouin scattering entails the nonlinear interaction between optical and mechanical fields inside a material. Achieving strong Brillouin interactions require simultaneous confinement of optical and mechanical modes, which remains challenging in SOI waveguides due to a strong phonon leakage towards the silica cladding.Since their first demonstration in silicon photonics [1, 2], subwavelength-grating metamaterials [3, 4] have been used as a powerful tool to realize high-performance silicon photonic devices. Indeed, subwavelength silicon nanostructures provide additional degrees of freedom to control the properties of photonic and phononic modes in silicon photonic circuits with remarkable flexibility [5]. In this invited presentation, we will show our most recent results the engineering of photons and phonons in silicon waveguides based on subwavelength structuration [6, 7]. We will also present the supercontinuum generation in the near-infrared and mid-infrared with suspended silicon waveguides [8].
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".