Enhanced diffraction efficiency with angular selectivity by inserting an optical interlayer into a diffractive waveguide for augmented reality displays
Bibliographic record
Abstract
The overall efficiency and image uniformity are important criteria for augmented reality display. The conventional in-coupling grating design intending to improve only the first-order diffraction efficiency without considering the multiple interactions with diffracted light in the waveguide is insufficient. In this work, the back-coupling loss (BCL) on the in-coupling surface relief grating, and the power of light arriving at the out-coupling grating over that of incident light (denoted as optical efficiency in waveguide, OEW) are introduced for the design of in-coupling grating. A simple and effective method to increase diffraction efficiency with unique angular selectivity is demonstrated by inserting an interlayer between the waveguide and grating. The optimized average OEW and its uniformity under a field of view of 40° are increased from 8.02% and 24.83% to 8.34% and 35.02% by introducing a region-selective MgF 2 interlayer.
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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".