Potential Enhancement of <i>f<sub>T</sub> </i> and <i>gₘf<sub>T</sub> </i>/<i>I<sub>D</sub> </i> via the Use of NCFETs to Mitigate the Impact of Extrinsic Parasitics
Bibliographic record
Abstract
The potential for negative-capacitance field-effect transistors (NCFETs) to enhance the unity-current-gain cutoff frequency${f}_{T}$and${g}_{m}{f}_{T}/{I}_{D}$ratio through a technique that mitigates the impacts of extrinsic parasitic capacitances is investigated, where${g}_{m}$is the transconductance and${I}_{D}$is the dc drain current and where “extrinsic” refers to elements arising outside an “intrinsic” or core transistor structure. We explain the technique and show that NCFETs can provide significant gains in extrinsic${f}_{T}$and${g}_{m}{f}_{T}/{I}_{D}$from this mitigation effect. However, an inherent degradation of intrinsic${f}_{T}$needs to be addressed to maximize these benefits, and this degradation can be alleviated through channel-length scaling as well as supply- and threshold-voltage tuning. The relative influences of different extrinsic parasitic elements are also evaluated, and it is found that improvements to${f}_{T}$and${g}_{m}{f}_{T}/{I}_{D}$increase as extrinsic parasitic capacitance increases and decrease as extrinsic parasitic resistance increases. Overall, this work finds that NCFET structures are promising candidates to mitigate the impacts of extrinsic parasitics in aggressively scaled transistors for the next-generation RF 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".