Bibliographic record
Abstract
High-frequency (HF) modeling of MOSFETs for radio-frequency (RF) integrated circuit (IC) design is discussed. Modeling of the intrinsic device and the extrinsic components is discussed by accounting for important physical effects at both dc and HF. The concepts of equivalent circuits representing both intrinsic and extrinsic components in a MOSFET are analyzed to obtain a physics-based RF model. The procedures of the HF model parameter extraction are also developed. A subcircuit RF model based on the discussed approaches can be developed with good model accuracy. Further, noise modeling is discussed by analyzing the theoretical and experimental results in HF noise modeling. Analytical calculation of the noise sources has been discussed to understand the noise characteristics, including induced gate noise. The distortion behavior of MOSFET and modeling are also discussed. The fact that a MOSFET has much higher "low-frequency limit" is useful for designers and modelers to validate the distortion of a MOSFET model for RF application. An RF model could well predict the distortion behavior of MOSFETs if it can accurately describe both dc and ac small-signal characteristics with proper parameter extraction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.014 | 0.009 |
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".