Analytical method for deriving consistent large–small‐signal field‐effect transistor model
Bibliographic record
Abstract
Abstract This article presents a detailed analytical method for deriving consistent large–small‐signal field‐effect transistor (FET) model. This resulted in a set of closed‐form equations relating the large‐signal model parameters to the small‐signal model ones. An improved equivalent circuit is proposed for modeling the transistor under large‐signal operation. In this circuit, RF nonlinear current sources are used to model the distributed effect of the gate–source and gate–drain junctions. The dispersion between DC and RF drain current characteristics is modeled using an improved back‐gating technique. The predictive model capabilities are illustrated with measured and simulated S‐parameters, output power at fundamental and harmonics frequencies of a commercial packaged GaAs FET device. The model is then fully validated by comparing measured and simulated results of output power, efficiency, and intermodulation distortion of a class AB amplifier designed at 1.9 GHz. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1001–1008, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27495
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".