Bibliographic record
Abstract
One concern as storage technology moves to perpendicular recording is the remnant state of the writer top pole. In principal, a remnant state with a substantial magnetization density perpendicular to the recording medium can lead to the unwanted erasure of data. Also, it is desirable to have the writer reach a nonerasing remnant state as quickly as possible. One technique to reduce the magnetization in the remnant state is to laminate the pole tip with some nonmagnetic material [Y. Satoh, A. Ohtsubo, and Y. Shimada, IEEE Trans. Magn. 21, 1551 (1985); S. Wang et al., IEEE Trans. Magn. 30, 3897 (1994)]. We have performed fully micromagnetic simulations of write elements with eight, five, four, and two laminates coupled antiferromagnetically. Results are presented for recording fields, as well as for the decay of the magnetization to a remnant state. The two- and four-laminate write elements typically have a vortex induced in the pole tip, and this vortex tends to survive, even in the remnant state. This can give rise both to a slow decay as well as large remnant fields from the out-of-plane magnetization in the vortex. On the other hand, the magnetization of the eight-laminate pole tip decays by “scissoring” of the magnetization in the laminates, with a faster decay to the remnant state. However, locally large divergences of the magnetization density can give rise to “hot spots” with relatively large remnant fields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".