An Image-Reject Low-Noise Amplifier with Passive Q-Enhanced Notch Filters
Bibliographic record
Abstract
An image-reject low-noise amplifier with passive Q-enhanced notch filters in 0.18 mum CMOS is presented. Available IR-LNA designs employ a single notch filter to reject the precise image frequency and therefore require an additional automatic tuning circuit. This design achieves image-rejection over a bandwidth by using two series-connected passive notch filters, thereby relaxing the requirement of any additional tuning circuit. The proposed image-reject low noise amplifier has 16 dB gain at the signal frequency of 2.4 GHz and 58 dB rejection over a bandwidth of 100 MHz centered at the image frequency of 1.6 GHz. Noise Figure of 2.25 dB and P1dB of -13 dBm are obtained with bias current of 3.9 mA drawn from a 1.5 V power supply.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".