The role of biasing electric field in intrinsic resistive switching characteristics of highly silicon-rich a-SiO<sub><i>x</i></sub> films
Bibliographic record
Abstract
The resistive switching behaviors are investigated in highly silicon-rich SiO x (x = 0.73) films, which are deposited using the plasma enhanced chemical vapor deposition method. For the Pt/SiO 0.73 /Pt structures, after the forming process the reset voltages (∼0.7 V) are lower than the set voltages (∼1.7 V). The metal-free structures N + -Si/SiO 0.73 /N ++ -Si/Pt show almost the same switching behaviors as those of Pt/SiO 0.73 /Pt structures, demonstrating an intrinsic resistive switching mechanism. We use the silicon dangling bonds (Si-DBs) percolation model to explain this. It is based on the biasing electric field decreasing the bond strength and leading to the breakage of Si–O bonds in SiO x films. Consequently the new Si-DBs are created and will contribute to form the dangling bonds percolation path. The temperature dependence of forming voltages is investigated. The forming voltages show no obvious changes and the forming process will occur as soon as the sweeping voltage reaches ∼10.5 V, even if the temperature decreases to 5.5 K. It indicates that the electric field plays an important role during the forming processes. Moreover, through the analysis of X-ray photoelectron spectroscopy and electron spin resonance signals, it can be concluded that the •Si≡Si 3 and •Si≡Si 2 O DBs centers are the main components in Si-DBs percolation path of SiO 0.73 films.
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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.
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