Enhanced resistive switching performance of TiO<sub>2</sub> based RRAM device with graphene oxide inserting layer
Bibliographic record
Abstract
Abstract In this work, graphene oxide (GO)/TiO 2 heterostructures for resistive random access memory devices were fabricated, and the composition and microstructure of TiO 2 and GO were characterized by x-ray diffraction, Raman spectroscopy, scanning electronic microscopy, and transmission electron microscopy. The resistive characteristics of the fabricated devices were investigated, and the remarkable improvement in cycle-to-cycle uniformity and high ON/OFF ratio of the TiO 2 thin film-based memory device were realized by introducing a thin GO layer. The formation/rupture of the conductive filament through the migration of oxygen vacancies in the TiO 2 substrate was responsible for the resistive switching. Owing to the different activation energies of reduction and oxidation of the GO, the set voltage became larger than the reset voltage. According to the linear fitting of double logarithm I – V plots, the conduction mechanism in low and high resistance states was governed by the ohmic mechanism and trap-controlled space charge limited current, respectively. The oxygen migration-induced oxidation/reduction in GO rendered it a good oxygen vacancy reservoir, which is responsible for the enhanced cycle-to-cycle uniformity and high ON/OFF ratio.
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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".