Weak-value-amplification enhancement of the magneto-optical Kerr effect in nanoscale layered structures
Bibliographic record
Abstract
The achievement of a larger magneto-optical Kerr effect (MOKE) in nanoscale layered structures is extremely important for both theoretical understandings and practical applications. However, nanoscale layered structures may not always exhibit enhanced MOKE under certain geometries. In this paper, we present a scheme based on weak-value amplification (WVA) for simultaneously detecting and enhancing the Kerr signals in nanoscale layered structures. The Kerr signals can be effectively amplified as the parameters of the preselection in WVA. We numerically investigate the dependence of the thickness $d$ of Co in the sample ${\text{HfO}}_{2}(10\phantom{\rule{0.16em}{0ex}}\text{nm})/\text{Co}(d\phantom{\rule{0.16em}{0ex}}\mathrm{nm})/{\mathrm{HfO}}_{2}(30\phantom{\rule{0.16em}{0ex}}\mathrm{nm})/\mathrm{Al}(40\phantom{\rule{0.16em}{0ex}}\mathrm{nm})/\mathrm{Si}$ and the sample $\text{Co}(d\phantom{\rule{0.16em}{0ex}}\mathrm{nm})/\mathrm{Si}$ in the range of 5 nm $<\phantom{\rule{4pt}{0ex}}d\phantom{\rule{4pt}{0ex}}<$ 50 nm. Our results indicate that the combination of the cavity and WVA can simultaneously amplify the MOKE signals when compared to the application of WVA on the sample $\text{Co}(d\phantom{\rule{0.16em}{0ex}}\mathrm{nm})/\mathrm{Si}$ and the application of traditional MOKE setup (TMOKES) on the two samples. Importantly, our results highlight that WVA maintains its ability to amplify MOKE signals even in cases where the cavity in the TMOKES scheme fails to enhance the Kerr signals. This signifies the exceptional advantage of WVA in amplifying Kerr signals over TMOKES, regardless of the sample structures and specific MOKE geometries employed.
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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 | 0.001 |
| 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".