Voltage-Driven All-Solid-State Ionic Control on Co/CoO Antiferromagnet/Ferromagnet Exchange Bias
Bibliographic record
Abstract
Spintronics traditionally relies on a large electric current to create magnetic fields or spin torques to manipulate magnetic properties, which inevitably leads to undesirable energy dissipation. Alternatively, the voltage control of magnetism (VCM) promises significantly lower energy costs. In the context of VCM, magneto-ionics distinguishes itself by leveraging voltage-driven ion transport as an energy-efficient approach to control magnetic properties, including magnetization, coercive field, and exchange bias (EB). Herein, we demonstrate that the voltage-driven ionic control of CoO antiferromagnetism allows manipulation of the magnetic properties in exchange-coupled ferromagnetic Co. In a "battery-like" device geometry, a 5 nm Co film is precisely oxidized to realize the Co/CoO heterostructure that is interfaced with a solid-state electrolyte and an anode-like Li ion source. The cathode-like CoO layer reversibly converts back and forth between Co and CoO under gate biases, even after 1000 cycles. This subsequently influences magnetic switching in the exchange-coupled Co layer, which is directly revealed by anisotropic magnetoresistance (AMR) in the Co channel. Our findings demonstrate an efficient method of all-solid-state, voltage-driven, highly reversible ionic control on magnetic channels, offering additional dimensions of control and mass integration capability for spintronic applications.
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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 | 0.000 |
| 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) | 0.001 | 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".