Understanding the Frequency- and Time-Dependent Behavior of Ballistic Carbon-Nanotube Transistors
Bibliographic record
Abstract
The high-frequency and time-dependent behavior of carbon-nanotube (CN) transistors is examined by numerically solving the time-dependent Boltzmann transport equation self-consistently with the Poisson equation. The two-port admittance matrix, containing the transistor'sy-parameters, is extracted. At frequencies below the transistor's unity-current-gain frequencyfT, they-parameters are shown to agree with those predicted from a quasi-static description of transistor operation, provided that the partitioning factor for the device charge is extracted through application of an appropriate time-dependent ramp voltage to the gate. The physics of time-dependent transport is described, and by examining the positive- and negative-going components of electron charge in the nanotube, it is shown for annindevice structure that thenregionscanadd a time delay to the device response, even though these regions do not affect the transistor's extrapolatedfT. For very high frequencies, or for very fast transients, it is pointed out that the conventional ldquofloating boundary conditionrdquo approach, which was originally suggested for dc simulations of ballistic Mosfets, becomes questionable when applied to time-dependent simulations of nanotubes. While this paper omits collisions and focuses on an intrinsic transistor structure that excludes external parasitics, it provides a first useful step toward the full frequency- and time-dependent characterization of CN transistors.
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 | 0.001 |
| 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 | 0.001 |
| 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".