A Technique for Differential Noise Figure Measurement of Differential LNAs
Bibliographic record
Abstract
This paper presents an approach to measure the noise figure of a differential low-noise amplifier (LNA) based on familiar ldquocold-hotrdquo single-ended noise figure measurements. To demonstrate the usefulness of this approach, measurement results are presented for a wideband differential LNA designed to be used as the first stage of the receiver in the Square Kilometre Array radio telescope. The presented LNA achieves less than 0.41 dB of differential noise figure in the 700 MHz to 1.4 GHz band, differential S1121between 18 and 14 dB, single-ended output P1 dB of -8.2 dBm, and output IP3 of -1 dBm while consuming 81 mA from a 1.3-V supply. The approach of measuring the differential noise figure may be automated with one switch at the output of a standard noise source and one switch at the input to a standard noise figure analyzer or a noise figure meter, allowing for automated noise figure measurements of differential LNAs based on the differential pair topology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".