Post-Processed Thin-Film GMI Magnetic Sensors
Bibliographic record
Abstract
In this paper, a multilayer thin-film giant magnetoimpedance (GMI) magnetic sensor is fabricated, and its performance is enhanced by post-processing treatment. The CoSiB alloy is developed to enhance the performance of multilayer thin-film GMI sensors. The material is studied for different post-processing magnetothermal conditions. The provided recipe will facilitate low-cost fabrication of GMI structures in conventional microfabrication facilities. A detailed study is carried out to investigate the effect of temperature, time, and external magnetic field on the results of the post-processing technique. The prepared samples are tested with various magnetics and material characterization tools in order to give a detailed understanding of these effects. The post-processing shows a significant impact on the magnetic properties of the material. It is demonstrated that some nanoclusters of cobalt (with a diameter of 2-4 nm) form in its amorphous matrix structure, enhancing the permeability of the material.
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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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".