Fabrication of blazed gratings by tilted reactive ion beam etching with the side mask for augmented reality applications
Bibliographic record
Abstract
A novel manufacturing method using the side mask and tilted reactive ion beam etching (RIBE) is proposed for the fabrication of blazed gratings. Electron beam lithography was carried out to pattern a groove with a nanoscale line width, and then appropriate mask materials were filled into the SiO2 trench by atomic layer deposition. After being etched by RIBE at specific tilted angles, the required blazed gratings were achieved. In contrast to the typical fabrication process with a patterned mask on top of the surface to be etched, V-shaped nano structures filled into the trenches were used as the side mask. During etching, the surface material located in the shadow of the side mask along incident ion beams was not etched. The depth and blazed angle of blazed gratings are determined by the height of the side mask and the mounting angle of the sample, respectively. In addition, the fabricated blazed gratings can be used as imprinting moulds to duplicate blazed gratings for augmented reality applications. Due to more options for side mask materials, this method is able to provide high repeatability and accurate controllability for fabricating blazed gratings.
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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) | 0.001 | 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 | 0.001 | 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".