A 4-mW monolithic CMOS LNA at 5.7GHz with the gate resistance used for input matching
Bibliographic record
Abstract
Design and measured results of a fully integrated 5.7-GHz CMOS low-noise amplifier (LNA) is presented. To design this LNA, the parasitic input resistance of a metal-oxide-semiconductor field-effect transistor (MOSFET) is converted to 50/spl Omega/ by a simple L-C network, hence eliminating the need for source degeneration. It is shown, by means of compact expressions, that this matching method enhances the effective transconductance of the LNA by a factor that is inversely proportional to a MOSFET's input resistance. The effect of our proposed method on the noise figure (NF) of the LNA is also discussed. With an 11.45-dB power gain and a 3.4-dB NF at 4mW of dc power, the presented LNA achieves the best overall performance when compared with the most recently published LNAs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".