Characterizing defects and transport in Si nanowire devices using Kelvin probe force microscopy
Bibliographic record
Abstract
Si nanowires (NWs) integrated in a field effect transistor device structure are characterized using scanning electron (SEM), atomic force, and scanning Kelvin probe force (KPFM) microscopy. Reactive ion etching (RIE) and vapor-liquid-solid (VLS) growth were used to fabricate NWs between predefined electrodes. Characterization of Si NWs identified defects and/or impurities that affect the surface electronic structure. RIE NWs have defects that both SEM and KPFM analysis associate with a surface contaminant as well as defects that have a voltage dependent response indicating impurity states in the energy bandgap. In the case of VLS NWs, even after aqua regia, Au impurity levels are found to induce impurity states in the bandgap. KPFM data, when normalized to the oxide-capacitance response, also identify a subset of VLS NWs with poor electrical contact due to nanogaps and short circuits when NWs cross that is not observed in AFM images or in current-voltage measurements when NWs are connected in parallel across electrodes. The experiments and analysis presented outline a systematic method for characterizing a broad array of nanoscale systems under device operation conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".