Study of ballasting carbon nanotube field emitter arrays with coaxial gate using doped silicon resistor
Bibliographic record
Abstract
One of the limitations of carbon nanotube (CNT) field emitter arrays (FEA) is the non-uniformity of total emission current contribution from each emitter due to geometry variation among CNT field emitters. We previously used a ballast resistor to ballast each emitter's current contribution to improve the reliability and stability of our CNT field emission (FE) cathode with coaxial gate. However, this approach has two main disadvantages: reduced FE current and enlarged power dissipation. In this paper, we improve the ballasting of our CNT FEA with coaxial gate using a doped silicon resistor. Each CNT field emitter is in series with a doped silicon resistor to avoid over current, based on the saturation of drift velocity in doped silicon. The FE current from each emitter can be limited at a desired current level based on the doping density and size of the doped silicon resistor. Those dominating emitter can be protected as gate voltage increases. The proposed approach is expected to improve our CNT cathode reliability and stability.
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".