RF parameter extraction of underlap DG MOSFETs: a look up table based approach
Bibliographic record
Abstract
In this study, a look up table (LUT) is developed to extract the intrinsic RF parameters of underlap DG MOSFET (UDG‐MOSFET) including the non‐quasi‐static (NQS) effect. The LUT‐based approach proposed; can accurately extract complex RF parameters of UDG‐MOSFET under different bias conditions, necessary for RF circuit simulations by an interpolation algorithm. The RF parameters including intrinsic gate to drain capacitance ( C gd ), gate to source capacitance ( C gs ), gate to drain resistance ( R gd ), gate to source resistance ( R gs ), gate to source transconductance ( g m ), drain to source transconductance ( g ds ), transport delay ( τ m ), capacitance because of DIBL ( C sdx ) and inductance because of transport delay ( L sd ), cut‐off frequency ( f T ) and maximum frequency of oscillation ( f max ) are extracted using LUT approach. Parameters extracted using LUT are compared with simulated data, considering the NQS effect, and are found in good agreement. For RF circuit applications a low‐noise amplifier is designed, with the UDG‐MOSFET, operating at a tuned frequency of 10 GHz.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".