Multilevel Nanoarray Spin–Orbit Torque Device for Process-in-Memory Applications
Bibliographic record
Abstract
The advancement of data-driven technologies has increased energy and time consumption in data transfer between processors and memory units, limiting further improvement in device performance. This challenge can be addressed by introducing process-in-memory (PIM) architecture, which alleviates data transfer overhead through in-memory computation. In this work, we propose multilevel nanoarray spin-orbit torque (SOT) devices for PIM applications. In a Hall bar structure with multiple ferromagnetic islands, the SOT switching current varies depending on the size or shape of each island. Discrete multilevel states can then be precisely controlled by modulating input current, thus demonstrating analog PIM functionality. Furthermore, the same device also enables logic operations, with pulse currents as digital inputs and multilevel resistances as digital outputs, thereby demonstrating its suitability for digital PIM applications. Notably, multilevel SOT switching can operate with nanosecond current pulses, without requiring external magnetic field, highlighting its potential for ultrafast, energy-efficient PIM platforms.
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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) | 0.003 | 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 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".