Assessment of High-Frequency Performance Limit of Black Phosphorus Field-Effect Transistors
Bibliographic record
Abstract
Recently, gigahertz frequencies have been reported with black phosphorus (BP) field-effect transistors (FETs), yet the high-frequency performance limit has remained unexplored. Here we project the frequency limit of BP FETs based on rigorous atomistic quantum transport simulations and the small-signal circuit model. Our selfconsistent nonequilibrium Green's function (NEGF) simulation results show that semiconducting BP FETs exhibit clear saturation behaviors with the drain voltage, unlike zerobandgap graphene devices, leading to >10 THz frequencies for both intrinsic cutoff frequency (fT) and unity power gain frequency (fmax). To develop keen insight into practical devices, we discuss the optimization of fTand fmaxby varying various device parameters such as channel length (Lch), oxide thickness, device width, gate resistance, contact resistance, and parasitic capacitance. Although extrinsic fTand fmaxcan be significantly affected by the contact resistance and parasitic capacitance, they can remain near THz frequency range (fT= 900 GHz; fmax= 1.2 THz) through proper engineering, particularly with an aggressive channel length scaling (Lch≈ 10 nm). Our benchmark against the experimental data indicates that there still exists large room for optimization in fabrication, suggesting further advancement of high-frequency performance of stateof-the-art BP FETs for the future analog and radio-frequency applications.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".