Nano-resolution Reconstruction of Magnetic Fields near a Magnetic Probe Using a Thin-film Magnetic Sensor
Bibliographic record
Abstract
This paper presents a nano-resolution measurement of magnetic fields near a magnetic probe using a thin-film magnetic sensor. The measurement method consists of a process, where the thin-film magnetic sensor is scanned over the sample, followed by a deconvolution scheme. In the scanning process, the thin-film magnetic sensor is scanned over the sample perpendicularly to the sensor. The sample is rotated horizontally under the sensor so that data measurements are obtained at different angles and positions. The deconvolution can be performed using existing methods, which are common in the field of computed tomography. An experiment was performed to verify the technique's capability of measuring a magnetic field using the thin-film magnetic sensor. Magnetic force microscopes are widely used for measuring magnetic fields. However, the magnetic field from the magnetic probe affects the magnetization of the sample when the sample's magnetization is small. Thin-film magnetic sensors do not have a magnetic field. Therefore the presented technique can measure small magnetic fields even from very sensitive magnetic samples. Currently, thin-film magnetic sensors are used as reading heads in hard drive disks (HDD). Types of heads used are thin-film inductive heads, anisotropic magnetoresistive (AMR) heads, and giant magnetoresistive (GMR) heads. Nowadays, the film thickness of the GMR head, which is most commonly used as the reading head in HDD, is less than 10nm. Previously, the resolution of the measurement was limited by the width of the sensor (ie. 100nm) which is always greater than the thickness. However, the new technique has the same resolution as the film thickness (ie. 10nm). The measurement techniques used in the HDD industry can be applied to our measurement method.
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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".