Combined Role of Substrate and Doping on the Semiconductor-to-Metal Transition of VO<sub>2</sub> Thin Films
Bibliographic record
Abstract
Vanadium dioxide exhibits a sharp temperature-induced structural change (monoclinic to rutile) that induces a semiconductor-to-metal transition together with a major change in its electrical properties. Even though VO 2 thin films are a promising candidate for numerous applications to electronic and energy devices, one of the greatest challenges toward integration of this material is to precisely control the transition temperature. Substrate-induced interfacial effects and dopant-induced isostatic stress combined with excess electron injection are the main paths used to reduce the temperature of transition. In this work, we combine metallic doping with tungsten atoms and strain engineering in VO 2 thin films in order to lower the transition temperature while maintaining a high resistivity contrast. Epitaxial undoped films are shown to respond to substrate-induced strain by relaxing with increasing thickness but with a limited reduction of the transition temperature. In contrast, epitaxial doped films do not show any dependence on substrate-induced strain but yield a great reduction of the transition temperature as compared to polycrystalline relaxed doped films. This temperature shifts down to room temperature or even close to 0 °C depending on the doping level. This shift is accompanied by a different morphology of the film as compared to undoped films. In contrast to undoped VO 2 where the substrate-induced effect is limited to very thin films, combining tungsten doping and substrate-induced strain allows one to take advantage of the interfacial effect at any film thickness. Moreover, activation energies below 100 meV and greatly reduced transition temperatures combined with an electrical contrast still above 2 orders of magnitude make tungsten-doped VO 2 films of strong interest for many devices.
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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".