Optimization of periodic column growth in glancing angle deposition for photonic crystal fabrication
Bibliographic record
Abstract
We investigate the growth of periodically aligned silicon microstructures for the fabrication of square spiral photonic crystals using the glancing angle deposition phi-sweep process. We report the optimization of the phi-sweep offset angle for fabrication of microstructures with more precise geometry. The effects of varying the sweep offset angle of the phi-sweep process are studied for films deposited onto a square lattice array of growth seeds. To represent one growth segment of the phi-sweep process, we fabricate 15 nm silicon thin films using several azimuthal substrate offsets from 0° to 45° at a vapor incidence angle of 85°. We also deposit silicon square spirals on square lattice arrays with the phi-sweep method, using various sweep offset angles from γ = 0° to 45°. We find that using an offset angle of γ = 26.5° optimizes the shadowing geometry, which minimizes anisotropic broadening, producing greater quality photonic crystal structures. From normal incidence reflection spectroscopy, a maximum full width at half-maximum of 273 ± 3 nm and a relative peak width (Δλ/λ) of 16.1 ± 0.1% were found for a sweep offset angle of γ = 26.5°.
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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) | 0.001 | 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".