Spin–orbit torque driven nano-oscillators based on synthetic Néel-like skyrmion in magnetic tunnel junction
Bibliographic record
Abstract
A synthetic skyrmion-based magnetic tunnel junction spintronic nano-oscillator is proposed. The oscillator consists of a Pt/Co/AlOx/Co heterostructure. It exploits the high-frequency eigenoscillations of a synthetic chiral nanomagnet, which is imprinted in the Pt/Co layer by the local manipulation of the magnetic anisotropy and interfacial Dzyaloshinskii–Moriya interaction. This synthetic nanomagnet has the spin texture equivalent to the Néel skyrmion, and its topological stabilization remains resilient with respect to the thermal fluctuations at finite temperatures. The oscillator is activated by spin Hall effect-induced spin–orbit torques, and an eigenoscillation with a frequency of ∼2.5 GHz is achieved. When the drive current exceeds a threshold value, the eigenfrequency shifts toward lower frequencies. This redshift is associated with the transition of skyrmion dynamics, in which its eigenmode evolves from the counter-clockwise rotation mode to a complex hybrid mode. Our result verifies the working performance of the proposed synthetic skyrmion-based oscillator and suggests promising prospects for using such artificial nanomagnets in future spintronic applications. It is also found that the synthetic skyrmions are topologically protected from annihilation under high drive currents and finite temperatures, and this resilience, thus, offers new opportunities to better design next generation skyrmion-based spintronic devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".