Characterization of Electrical Properties of Suspended ZnO Nanowires Using a Nanorobotic Manipulation System Inside a Scanning Electron Microscope for Nanoelectronic Applications
Bibliographic record
Abstract
Electrical characterization of semiconducting oxide nanowires (NWs) is mostly performed using complex techniques, which necessitates a series of costly nanofabrication procedures. In this work, with the aim to provide a low-cost, feasible, facile, and reproducible approach for enabling the study of NW electrical properties, we report direct electrical measurements on individual and overlapped suspended zinc oxide NWs (ZnO NWs). We have succeeded in constructing both two- and three-terminal devices simply by employing tungsten (W) nanoprobes with the aid of a nanomanipulation system embedded inside a scanning electron microscope’s vacuum chamber. Stable contacts were established using the Joule heating effect and e-beam exposure at the junctions between the NW and the pre-cleaned W tips. P-channel field-effect transistor devices were achieved with an on–off current ratio of ∼10 1, a threshold voltage (>1.5 V), a transconductance of ∼16 μS, a sub-threshold swing of ∼220 mV/decade, and field-effect carrier mobility roughly estimated to be around 926.4 cm 2 /(V·s) after correction for contact resistances/optimization. The average resistivity of ZnO NWs was calculated to be ∼2.23 × 10 –2 Ω·cm for NWs with diameters between 70 and 500 nm. Besides, we have demonstrated a contact resistance of ∼19.60 kΩ and a Schottky barrier height of ∼0.37 eV present at W/ZnO NW interfaces. The contact resistance between two overlapped ZnO NWs was estimated to be ∼283 kΩ, which is relatively higher than that offered between W/ZnO NWs. This work provides a solid experimental procedure to address true intrinsic electrical properties at metal/semiconductor interfaces, and our findings have potential applications in next-generation 3D suspended ZnO NW-based nanoelectronic 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".