Ultrathin TiO<i><sub>x</sub></i> Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors
Bibliographic record
Abstract
Abstract One‐dimensional semiconductor nanowires have been widely used as important building blocks in a number of devices. However, the performance of these devices is seriously hindered by the surface states/defects on the nanowires, which is a great obstacle to the realization of controllable and predictable characteristics. The introduction of an ultrathin metal oxide layer between Au electrodes and a ZnO nanowire is used to eliminate the surface effects of the nanowires, leading to improved volatile threshold switching performance. Study of the conduction mechanism demonstrates that the TiOx interfacial layer functions as a barrier between the electrodes and the nanowire, wherein the oxygen defects provide localized trap sites for electron hopping in a low electric field as well as assisting electron tunneling in a high electric field. Several key synaptic functions, including excitatory current response, facilitation and depression, and short‐term plasticity, are realized with this modified nanowire device, which is promising for neuromorphic computing applications. Interface engineering between electrodes and semiconductor nanowires can help in the realization of high‐performance memristive devices, allowing new approaches to the assembly of neuromorphic systems with nanometer‐sized features.
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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".