A 0.1–20.1-GHz Wideband Noise-Canceling g<sub>m</sub>-Boosted CMOS LNA With Gain-Reuse
Bibliographic record
Abstract
This article presents a novel wideband low-noise amplifier (LNA) topology that incorporates noise cancellation in a$g_m$-boosted common gate (CG) LNA by reusing the inverting amplifier used for$g_m$-boosting as a parallel gain stage A$g_m$-boosted CG stage provides the wideband input matching while the current reuse (CR) inverting amplifier is simultaneously used for boosting$g_m$, improving gain, and canceling noise. Shunt and series inductive peaking techniques are implemented to extend the bandwidth of the LNA. The LNA is fabricated in Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm CMOS process and occupies a die area of 0.263 mm2. The measurement results indicate the combination of these techniques produces an LNA with a 20-GHz bandwidth, an average gain of 12 dB, an average noise figure (NF) of 3.87 dB, and a 2.53-dBm peak input-referred third-order intercept point (IIP3) while consuming 13.2 mW at 1.2 V, resulting in the highest figure of merit (FoM) among the reported state-of-the-art 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".