Periodic magnetic microstructures by glancing angle deposition
Bibliographic record
Abstract
An advanced deposition technique known as glancing angle deposition (GLAD) [K. Robbie, J. C. Sit, and M. J. Brett, J. Vac. Sci. Technol. B 16, 1115 (1998); K. Robbie and M. J. Brett, U.S. Patent No. 5,866,204 (filed 1999)] has been used to fabricate periodic arrays of magnetic pillars and randomly seeded magnetic helices, posts, and chevrons. Because of the nature of initial film nucleation, the GLAD process normally distributes posts randomly on the substrate surface. We can grow periodic arrays of GLAD microstructures by suppressing the randomness inherent within the initial nucleation stage of film growth. Shadowing sites were fabricated by pre-patterning a thin titanium layer on silicon substrates into a square array using electron beam lithography. These sites shadow regions of the substrate from incident flux during film deposition and act as preferred nucleation sites for film growth. Using this process, we have fabricated periodic arrays of cobalt posts with a regular elemental period of 600 nm and post diameters and heights of 300 and 400 nm, respectively. Randomly seeded posts, helices, and chevrons were also fabricated. The mean separation for the randomly seeded posts was 350 nm with individual post diameters of 100–150 nm, while the separations for the helices and chevrons were less than 100 nm. X-ray diffraction, transmission electron microscopy, and a dc superconducting quantum interference device magnetometer were used to analyze the magnetic and crystal properties of both the periodic and randomly seeded arrays. A newly developed three-dimensional ballistic deposition simulator was used to simulate the growth of the periodic post arrays in order to better understand the growth mechanisms.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".