Contact engineering of single core/shell SiC/SiO<sub>2</sub> nanowire memory unit with high current tolerance using focused femtosecond laser irradiation
Bibliographic record
Abstract
Single nanowire memory units are of particular interest in the design of high-density nanoelectronic circuits, but the performance due to weak contact state remains a major problem. In this paper, bonding between core/shell SiC/SiO2 nanowire and Au electrodes can be improved via local contact engineering with femtosecond (fs) laser irradiation. An optimized heterojunction (Au-SiO2-SiC) is possible since plasmonic enhanced optical absorption can be localized at the metal-oxide (Au-SiO2) interface. Electron transport across the barrier and charge accumulation at the oxide-semiconductor (SiO2-SiC) interface are improved in nanowire circuits. A fast and stable resistance change can be achieved after only one biasing cycle ('write') and the written state can be read/extracted at a low voltage (∼ 0.5 V). Unlike other as-built nanowire circuits, the resistance state can be retained for 10 min in the absence of external power, indicating that these devices can be used for short-term memory units. High current tolerance is also provided in the circuit by the surface oxide shell which acts to protect the inner SiC core. The current density carried by the single SiC/SiO2 nanowire circuit can be as high as ∼3 × 106 A cm-2 before break down, and that breakdown occurs as a two-stage process.
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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".