Induced Complementary Resistive Switching in Forming-Free TiO<sub><i>x</i></sub>/TiO<sub>2</sub>/TiO<sub><i>x</i></sub> Memristors
Bibliographic record
Abstract
The undesirable sneak current path is one of the key challenges in high-density memory integration for the emerging cross-bar memristor arrays. This work demonstrates a new heterojunction design of oxide multilayer stacking with different oxygen vacancy contents to manipulate the oxidation state. We show that the bipolar resistive switching (BRS) behavior of the Pt/TiO x /Pt cross-bar structure can be changed to complementary resistive switching (CRS) by introducing a thin TiO 2 layer in the middle of the TiO x layer to obtain a Pt/TiO x /TiO 2 /TiO x /Pt device architecture with a double-junction active matrix. In contrast to the BRS in a single-layer TiO x matrix, the device with a double-junction matrix remains in a high-resistance state in the voltage range below the SET voltage, which makes it an efficient structure to overcome the sneak path constraints of undesired half-selected cells that lead to incorrect output reading. This architecture is capable of eliminating these half-selected cells between the nearby cross-bar cells in a smaller programming voltage range. A simplified model for the switching mechanism can be used to account for the observed high-quality switching performance with excellent endurance and current retention properties.
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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".