Tuning of interlayer exchange coupling in Ni80Fe20/Ru/Ni80Fe20 nanowires
Bibliographic record
Abstract
In this work, we demonstrate how the static and dynamic properties of Ni80Fe20/Ru/Ni80Fe20 nanowires can be tuned by varying the Ru spacer layer thickness. Specifically, changing the Ru thickness we have tuned the Ruderman-Kittel-Kasuya-Yosida exchange interaction, and thus the antiferromagnetic (AFM) strength between the Ni80Fe20 layers. We show that there is a strong correlation between the interlayer coupling and features in ferromagnetic resonance (FMR) modes. We found different mode-softening degree of the FMR curves as function of the strength of AFM coupling, together with a clear frequency gap at around zero field. These experimental results are in qualitative agreement with presented micromagnetic simulations that also include biquadratic interface exchange. Understanding these characteristics may offer insights for reconfigurable vertical magnetic logic devices and microwave filters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".