Spatially Correlated Oxygen Vacancies, Electrons and Conducting Paths in TiO<sub>2</sub> Thin Films
Bibliographic record
Abstract
Resistive switching, characterized by reversible changes in material resistance under external electric fields, underpins resistive random-access memory (ReRAM) technology, which holds promise for next-generation memory and neuromorphic devices owing to its fast switching speed, nonvolatility, and structural simplicity. Among materials exhibiting resistive switching, transition metal oxides emerge as leading candidates for ReRAM components due to their high CMOS compatibility. However, complex thermal, electrical, chemical, and mechanical interactions during switching introduce variability, leaving the underlying mechanisms insufficiently understood. Therefore, this study investigates the ionic-electronic dynamics involved in resistive switching, focusing on the electroforming and reset processes in TiO 2 thin films─a representative transition metal oxide─through a colocalized, multimodal scanning probe microscopy (SPM) approach. Conductive atomic force microscopy (C-AFM) induces resistive switching and visualizes modulated spatial current pathways, while electrochemical strain microscopy (ESM) and Kelvin probe force microscopy (KPFM) capture corresponding ionic and electronic interplays at the same switching event and site. This integrated strategy provides direct nanoscale correlations that are difficult to resolve with single-mode or separate modality measurements, revealing how defect ion modulation and electron injection in concert govern the switching behavior. Furthermore, topography degradation observed during reset processes suggests that facilitated diffusion of injected oxygen ions along defect-enriched sites enhances retention properties of high resistance states. Based on these findings, the study proposes a potential switching mechanism, emphasizing the role of ionic-electronic dynamics.
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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".