A Sub-mW, Ultra-Low-Voltage, Wideband Low-Noise Amplifier Design Technique
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a design methodology for an ultra-low-power (ULP) and ultra-low-voltage (ULV) ultra-wideband (UWB) resistive-shunt feedback low-noise amplifier (LNA). The ULV circuit design challenges are discussed and a new biasing metric for ULV and ULP designs in deep-submicrometer CMOS technologies is introduced. Series inductive peaking in the feedback loop is analyzed and employed to enhance the bandwidth and noise performance of the LNA. Exploiting the new biasing metric, the design methodology, and series inductive peaking in the feedback loop, a 0.5 V, 0.75-mW broadband LNA with a current reuse scheme is implemented in a 90-nm CMOS technology. Measurement results show 12.6-dB voltage gain, 0.1-7-GHz bandwidth, 5.5-dB NF, -9-dBm IIP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , and -18-dB P1dB while occupying 0.23 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it