Applications of Body Biasing in Multistage CMOS Low-Noise Amplifiers
Bibliographic record
Abstract
Low-noise amplifiers (LNAs) are one of the important building blocks of wireless receivers. LNA design parameters such as gain, noise figure, linearity, input matching, and stability are important metrics and typically affect the overall performance of the receiver. The strong trade-offs among these design parameters often necessitate several design iterations. While many of these trade-offs are due to the nature of the circuit and are inevitable, it is desirable to decouple the effects of each parameter on the others. In this work, body biasing is introduced as a technique to enhance the linearity, to improve the noise figure and to provide gain variation. These techniques are presented in the context of a three-stage LNA. By applying body biasing in each stage, noise figure, gain variation and linearity of the overall amplifier are adjusted almost independently, i.e., with minimal interrelation among these design parameters. As a proof-of-concept, a prototype 4.4-GHz LNA is designed and fabricated in a 0.13- μm CMOS technology. The LNA achieves a minimum noise figure of 3.8 dB, maximum gain of 20.2 dB, and a maximum IIP3 of -14 dBm while consuming 3.6 mW from a 1.2 V supply.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".