Finite difference simulation of thermally tuned hexagonal photonic crystals
Bibliographic record
Abstract
Thermal tuning of hexagonal photonic crystals by absorption of laser energy is examined through finite difference numerical simulation. The photonic crystals are patterned in the device layer of the silicon on insulator (SOI) platform. The thermal equations, which include contributions from laser absorption gain, conduction loss, and radiation loss are combined to obtain a heat balance equation. This governing equation is modeled using a thermodynamic finite difference computation engine. To ensure the stability of the thermal model within the transient regime the velocity of heat propagation is calculated and included as a courant factor controlling the coarseness of the discretization grid and time step interval. The thermal distribution obtained from the numerical simulation, combined with the thermo-optic effect, can be used to alter the initial dielectric distribution of the device layer. The integration of the change in refractive index into the existing dielectric enables the thermal effects to be included into a standard optical finite difference time domain (FDTD) engine. Through the implementation of the optical and thermal simulation tools, the laser thermal tuning of the band gaps and localized states of hexagonal photonic crystals will be explored. The temperature dependence of the central wavelength of the localized states will be calculated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".