Enhancement in broadband and quasi-omnidirectional antireflection of nanopillar arrays by ion milling
Bibliographic record
Abstract
A new technique is developed to fabricate biomimetic antireflection coatings (ARCs). This technique combines a bottom-up fabrication approach (glancing angle deposition, or GLAD) with a top-down engineering process (ion milling). The GLAD technique is first utilized to produce nanopillar arrays (NPAs) with broadened structures, which are subsequently transformed into biomimetic tapered geometries by means of post-deposition ion milling. This structure transformation, due to milling-induced mass redistribution, remarkably decreases reflection over a wide wavelength range (300-1700 nm) and field of view (angle of incidence < 60° with respect to the substrate normal). The milling-induced antireflection enhancement has been demonstrated in the NPAs made of Si, SiO(x) and TiO(2), illustrating that this integrated technique is readily adapted to a wide variety of materials. Good agreement between simulation and experiment indicates that the enhanced antireflection performance is ascribed to a smoother refractive index transition from the substrate to the air, which improves the impedance match and reduces reflection losses. Additionally, ion bombardment tends to alter the stoichiometry and diminish the crystallographic structure of the NPA materials. The broadband and quasi-omnidirectional antireflection observed establishes the strong competitiveness of this technique with the methods previously reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".