20.9 A 1.92mW filtering transimpedance amplifier for RF current passive mixers
Bibliographic record
Abstract
Nowadays, current passive mixers represent the state of the art for signal down-conversion in wireless receivers. In such kind of structures, noise, distortions and losses are strictly correlated to the performance of the stage following the mixer. The most common solution adopted to sense the down-converted current is a transimpedance amplifier (TIA) in shunt with a capacitance to ground that assures a low input impedance when the loop gain of the amplifier decreases (Fig. 20.9.1a). A low input impedance is necessary to have a small voltage swing at the output of the mixer (typically few hundreds mV) to minimize the modulation of the switch resistance and with it the distortion produced during the downconversion. The input capacitance can also be used to filter the majority of the out-of-band interferers by transforming the TIA into a filter [1,2] (Fig. 20.9.1b). This reduces the dynamic range required by the TIA and its power consumption. This advantage comes at a cost of area, since the limited voltage swing tolerable at the input of the TIA demands a large capacitor to absorb the downconverted interferers. This trade-off is relaxed with the proposed solution (Fig. 20.9.2), where the input capacitance (C1) is partially boosted by a feedback network minimizing the swing required at the input of the TIA. This idea, originally proposed in [3] only to improve the 1dB compression point, is now used to maximize the spurious-free dynamic range of the TIA, exploiting an intrinsic in-band highpass shaping of noise and distortion. Furthermore, an adaptive transfer function, which improves its filtering action in presence of large out-of-band interferers, is realized.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.029 |
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".