Superconducting NbN for Strong Magnetic Field Applications: Impact of the Thin Films Intrinsic Disorder
Bibliographic record
Abstract
The research on superconducting circuits for quantum information technologies progressively extends from the academic world towards the industry. Material sciences therefore become an essential lever for the development of technologies based on such circuits as they allow finding industry-compatible routes of improvements. In this context, we propose a study on the behavior of the niobium nitride (NbN), one of the most commonly used material for these devices, when exposed to strong magnetic fields. To this aim, the properties of NbN layers with the same composition but different microstructures, tuned by changing the deposition method, are compared. This study aims to establish interdependencies between the microstructure of the material and its behavior once exposed to a magnetic field. X-ray diffraction and Hall-effect characterizations are used to assess that the microstructure is significantly modified by the choice of the deposition technique. Pushing further these investigations also allowed to quantify and compare the level of disorder in both cases by extracting the Ioffe-regel and the Ginzburg-Landau parameters from characterizations of the superconducting transition temperature for several magnetic field amplitudes. This was used to conclude that in highly disordered NbN layers, the microstructure heterogeneities are responsible for a strong electron localization allowing to significantly enhance the resilience of their superconducting state under strong magnetic fields.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".