Reliable and Low-Power Multilevel Resistive Switching in TiO<sub>2</sub> Nanorod Arrays Structured with a TiO<sub><i>x</i></sub> Seed Layer
Bibliographic record
Abstract
The electrical performance of TiO 2 nanorod array (NRA)-based resistive switching memory devices is examined in this paper. The formation of a seed layer on the fluorine-doped tin oxide (FTO) glass substrate after treatment in TiCl 4 solution, before the growth of TiO 2 NRAs on the FTO substrate via a hydrothermal process, is shown to significantly improve the resistive switching performance of the resulting TiO 2 NRA-based device. As fabricated, the Al/TiO 2 NRA/TiO x layer/FTO device displayed electroforming-free bipolar resistive switching behavior while maintaining a stable ON/OFF ratio for more than 500 direct sweeping cycles over a retention period of 3 × 10 4 s. Meanwhile, the programming current as low as ∼10 –8 A and 10 –10 A for low resistance state and high resistance state respectively makes the fabricated devices suitable for low-power memristor applications. The TiO x precursor seed layer not only promotes the uniform and preferred growth of TiO 2 nanorods on the FTO substrate but also functions as an additional source layer of trap centers due to its oxygen-deficient composition. Our data suggest that the primary conduction mechanism in these devices arises from trap-mediated space-charge-limited current (SCLC). Multilevel memory performance in this new device is achieved by varying the SET voltage. The origin of this effect is also discussed.
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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".