Effect of Parasitic Capacitance on GMI Magnetic Sensor Performance
Bibliographic record
Abstract
Magnetic sensors based on GMI devices are the subject of intensive research, as they appear promising for magnetometry applications. Performances of GMI magnetometers are often limited by the noise of the electronic setup. Thus, the present challenge is to increase the GMI device sensitivity (expressed in V/T) in order to decrease the equivalent magnetic noise of the system. In our previous work, we showed that the use of a pick-up coil in an off-diagonal configuration improves the magnetic sensor sensitivity and offers a promising approach for developing an inexpensive magnetometer with sub-pT/Hz equivalent magnetic noise levels. Ideally, the use of a coil increases the sensitivity linearly as a function of the number of turns. However, this effect is reduced by the parasitic capacitance of the coil. This affects the device sensitivity, noise level and system performance. The parasitic capacitance can degrade all of these, but also induces a resonance effect, which can help to optimize magnetometer sensitivity, and thus, its noise level. We analyze the effects of the parasitic capacitance on the system (sensitivity and noise) and propose optimization routes. We have obtained sensor sensitivity as high as 700 fT/Hz.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".