Bibliographic record
Abstract
This paper presents a through description of radio frequency (RF) noise characterization and modeling of CMOS transistors. It begins with the definition of the four noise parameter of a two-port network - minimum noise figure (NFmin), equivalent noise resistance (Rn), optimized source impedance (Ropt) and optimized source reactance (Xopt). These four parameters are used in device characterization and it is shown how they can be calculated by using the noise two-port network theory and a circuit simulator. Then two de-embedding procedures are discussed in detail for noise and scattering parameter de-embedding to get rid of the parasitic effects from the probe pads and interconnections in the device-under-test (DUT). Ideally there is no frequency and geometry limitation for the method based on a cascade configuration. Methods to directly extract the channel noise, induced gate noise and their correlation from the RF and noise measurements are developed and the extracted noise sources as a function of frequency and bias condition for different channel lengths a presented. Some design consideration for the design of low noise circuits - how to select the device size, choice of DC bias conditions and design device layout, are presented. Finally, some published noise models for the channel noise, induced gate noise and their correlation are discussed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".