Helium focused ion beam direct milling of plasmonic heptamer‐arranged nanohole arrays
Bibliographic record
Abstract
Abstract We fabricate plasmonic heptamer‐arranged nanohole (HNH) arrays by helium (He) focused ion beam (HeFIB) milling, which is a resist‐free, maskless, direct‐write method. The small He + beam spot size and high milling resolution achieved by the gas field‐ionization source used in our HeFIB allows the milling of high aspect ratio (4:1) nanoscale features in metal, such as HNHs incorporating 15 nm walls of high verticality between holes in a 55‐nm‐thick gold film. Drifts encountered during the HeFIB milling of large arrays, due to sample stage vibrations or He beam instability, were compensated by a drift correction technique based on in situ He ion imaging of alignment features. Our drift correction technique yielded 20 nm maximum dislocation of HNHs, with 6.9 and 4.6 nm average dislocations along the horizontal and vertical directions, respectively. The measured optical resonance spectra of the fabricated plasmonic HNH arrays are presented to support the fabrication technique. Defects associated with HeFIB milling are also discussed.
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".