Wideband LNA Employing Intrinsic Feedback and Back-Gate Resistance for Noise and Input Power Matching
Bibliographic record
Abstract
This article investigates two low-noise-amplifier (LNA) topologies that provide wide input-power-matched bandwidths and low noise figures. These topologies offer the distinct advantage of eliminating the need for elaborate matching networks at the LNA input. Instead, they utilize intrinsic feedback via gate–drain networks and/or the resistance of the SOI-transistor back-gate terminal to realize the real part of the input impedance. These topologies are experimentally demonstrated with two 22-nm FDSOI LNAs. An LNA matched with the assistance of the gate–drain network exhibits bandwidth from 7.7 to 33.3 GHz, which is further improved to 6–38.7 GHz through the application of the back-gate-resistance method. The two LNAs exhibit noise-figure minima of 1.8 and 1.9 dB, maximum gains of$\text{{14.7}}$and${15.6}~\text{{dB}}$, and maximum IP1dBs of$-\text{{9.1}}$and$-\text{{7.8~dBm}}$while consuming 10 and 7.8 mW of power (from 0.8-and 0.4-V supplies) and occupying$\text{{0.04}}$and$\text{{0.03~mm}}^{{2}}$of active areas, respectively.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".