RF Linearity Performance Potential of Short-Channel Graphene Field-Effect Transistors
Bibliographic record
Abstract
The radio-frequency (RF) linearity performance potential of short-channel graphene field-effect transistors (GFETs) is assessed by using a nonlinear small-signal circuit model under the first approximation of ballistic transport. An intrinsic GFET is examined to reveal the key features of GFET linearity, and extrinsic parasitics are then included to assess the overall RF linearity. It is shown that short-channel GFETs can be expected to have a signature behavior versus gate bias that includes a constant-linearity region at low gate bias, sweet spots of high linearity before and after the gate bias for peak cutoff frequency, and poor linearity at the gate bias corresponding to the peak cutoff frequency. It is otherwise found that a GFET offers overall linearity that is comparable to a MOSFET and a CNFET, with the exception that the amount of intermodulation distortion in a GFET is dominated by the drain-injected carriers, a unique outcome of graphene's lack of a bandgap. Qualitative agreement with experiment in the signature behavior of GFET linearity supports the approach and conclusions.
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) | 0.001 | 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".