Weak-value-amplification enhancement of the Magneto-optical Kerr effect in nanoscale layered structures
Bibliographic record
Abstract
Abstract The achievement of the larger Magneto-optical Kerr rotations in nanoscale layered structures is extremely important for both theoretical understandings and practical applications. In this paper, we propose a scheme for simultaneously detecting the Kerr rotation and the Kerr ellipticity based on the weak-value amplification (WVA) technique. In the Magneto-optical Kerr effect study for the layered structure, the Kerr rotation and the Kerr ellipticity are obtained by calculating the boundary matrix and the transmission matrix of the layered structure. Then, the Kerr rotation and the Kerr ellipticity can be effectively amplified as the parameters of the pre-selection in the standard weak measurement. In addition, we numerically investigate the dependence of the thickness d of Co in the nanoscale layered structure HfO2 (10~nm)/Co(d nm)/ HfO2 (30 nm)/Al(40 nm)Si films and the Co(d nm)Si films at the measurement range of 5 nm < d < 50 nm. Simulation results confirmed that the Kerr rotation and the Kerr ellipticity can be effectively amplified by choosing the appropriate post-selected state, especially when the nanoscale layered structure shows no advantage for enhancing Kerr signals over the Co(d nm)/Si films without the weak-value-amplification technique. The realization of enhancement of Kerr signals in a nanoscale layered structure may have an important application in the magneto-optic (MO) parameters measurement and the MO properties in a more complex nanoscale structure.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".