Applications of Body Biasing in Multistage CMOS Low-Noise Amplifiers
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it