High-Performance Single-Active-Layer Memristor Based on an Ultrananocrystalline Oxygen-Deficient TiO<sub><i>x</i></sub> Film
Bibliographic record
Abstract
The theoretical and practical realization of memristive devices has been hailed as the next step for nonvolatile memories, low-power remote sensing, and adaptive intelligent prototypes for neuromorphic and biological systems. However, the active materials of currently available memristors need to undergo an often destructive high-bias electroforming process in order to activate resistive switching. This limits their device performance in switching speed, endurance/retention, and power consumption upon high-density integration, due to excessive Joule heating. By employing a nanocrystalline oxygen-deficient TiO x switching matrix to localize the electric field at discrete locations, it is possible to resolve the Joule heating problem by reducing the need for electroforming at high bias. With a Pt/TiO x /Pt stacking architecture, our device follows an electric field driven, vacancy-modulated interface-type switching that is sensitive to the junction size. By scaling down the junction size, the SET voltage and output current can be reduced, and a SET voltage as low as +0.59 V can be obtained for a 5 × 5 μm 2 junction size. Along with its potentially fast switching (over 10 5 cycles with a 100 μs voltage pulse) and high retention (over 10 5 s) performance, memristors based on these disordered oxygen-deficient TiO x films promise viable building blocks for next-generation nonvolatile memories and other logic circuit systems.
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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".