Numerical band calculation of holographically formed periodic structures with irregular motif
Bibliographic record
Abstract
Recently, two-dimensional and three-dimensional periodic dielectric structures have been directly fabricated by laser holographic lithography (HL) to create novel geometric structures with high-precision tolerances. Multiple beam interference via beam splitting mirrors or diffractive optical elements produce isointensity contours that can be accurately recorded in photoresist and subsequently used as a template for creating photonic crystals with a complete or partial bandgap. The periodic structures typically formed by HL comprise of highly convoluted contours that do not conform to typically known geometrical shapes and therefore preclude the use of analytic approaches such as the plane wave expansion (PWE) method to accurately generate the band-dispersion curves. In this paper, we present a numerical technique that decomposes the HL-formed structure into fine mesh grids and expands this material mesh into the PWE method to generate band-dispersion curves. Band diagrams obtained in this way are shown to accurately match the well known solutions for opal, inverted opal, and woodpile structures which have a regular motif. We extend the numerical technique to predict the band structure of HL templates which have an irregular motif and present band diagrams for structures formed by Ar-ion laser phasemask interference.
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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) | 0.000 | 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 | 0.001 | 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".