GMW Best Poster Award 2 - Sensing Stray Fields From Magnetic Nanocircuits With Nitrogen-Vacancy Defects
Bibliographic record
Abstract
Authors: A. Solyom, M. Caouette-Mansour, B. Ruffolo, L. Childress, J. Sankey, Physics, McGill University, Montreal, Quebec, CANADA|P.M. Braganca, Western Digital Corp, San Jose, California, UNITED STATES|M. Pioro-Ladrière, Institut Quantique and Département de Physique, Universite de Sherbrooke, Sherbrooke, Quebec, CANADA|M. Pioro-Ladrière, Quantum Information Science Program, Canadian Institute for Advanced Research, Toronto, Ontario, CANADA| Abstract Body: We discuss our latest efforts toward using a single, optically active nitrogen-vacancy (NV) spin sensor (implanted in a single-crystal diamond substrate) to measure magnetic thermal noise modified by spin Hall torques [1] in a Py/Pt nanowire [2]. In this poster, we first present our subtractive method for fabricating magnetic nanocircuits on diamond, and initial characterization of working Py(5 nm)/Pt(5 nm) nanowires. We reliably achieve contacts with few-ohms of resistance and 200 nm of overlap at each end of the 2-μm-long, 400-nm-wide wire, and we observe an anisotropic magnetoresistance of 0.4%. Importantly, we observe that the subtractive patterning process (masked ion milling) used to define the devices reduces the NV spin resonance contrast by a factor of ∼5, but that subsequent exposure to an oxygen plasma returned the contrast to its nominal level without affecting the nanowire behavior. Finally, we present progress toward using transport measurements to estimate thermal time scales in these systems (while calibrating the RF current in the nanowire), as well as preliminary NV readout of spin-transfer-controlled magnetic thermal noise.References: [1] C. Du, T. van der Sar, et al., Science, 357, 195-198 (2017) [2] A. Solyom, Z. Flansberry, et al., Nano Lett., 18, 6494-6499 (2018)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.178 | 0.073 |
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".