Photonic band-gap formation by optical-phase-mask lithography
Bibliographic record
Abstract
We demonstrate an approach for fabricating photonic crystals with large three-dimensional photonic band gaps (PBG's) using single-exposure, single-beam, optical interference lithography based on diffraction of light through an optical phase mask. The optical phase mask (OPM) consists of two orthogonally oriented binary gratings joined by a thin, solid layer of homogeneous material. Illuminating the phase mask with a normally incident beam produces a five-beam diffraction pattern which can be used to expose a suitable photoresist and produce a photonic crystal template. Optical-phase-mask Lithography (OPML) is a major simplification from the previously considered multibeam holographic lithography of photonic crystals. The diffracted five-beam intensity pattern exhibits isointensity surfaces corresponding to a diamondlike (face-centered-cubic) structure, with high intensity contrast. When the isointensity surfaces in the interference patterns define a silicon-air boundary in the resulting photonic crystal, with dielectric contrast 11.9 to 1, the optimized PBG is approximately 24% of the gap center frequency. The ideal index contrast for the OPM is in the range of 1.7-2.3. Below this range, the intensity contrast of the diffraction pattern becomes too weak. Above this range, the diffraction pattern may become too sensitive to structural imperfections of the OPM. When combined with recently demonstrated polymer-to-silicon replication methods, OPML provides a highly efficient approach, of unprecedented simplicity, for the mass production of large-scale three-dimensional photonic band-gap materials.
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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) | 0.000 | 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 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".