Novel modeling and dynamic simulation of magnetic tunnel junctions for spintronic sensor development
Bibliographic record
Abstract
Abstract Spintronic magnetic sensors with the integration of magnetic materials and microstructures have been enabling people to make use of the electron spin and charge properties in many applications. The high demand for such sensors has in turn spurred the technology developments in both novel materials and their atomic-level controls. Few works, however, have been carried out and reported thus far in modeling and simulation of these spintronic magnetic sensing units based on magnetic tunnel junction (MTJ) technology. Accordingly, this paper proposes a novel modeling approach as well as an iterative simulation methodology for MTJs. A more comprehensive electrical tunneling model is established for better interpreting the conductance and current generated by the electron tunneling, and this model can also facilitate the iterative simulation of the micromagnetic dynamics. Given the improved tunneling model as well as the updated dynamic simulation, the electric characteristics of an MTJ with an external magnetic field can be conveniently computed, which provides a reliable benchmark for the future development of novel spintronic magnetic sensors.
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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".