Wideband CMOS Amplification Stage for a Direct-Sampling Square Kilometre Array Receiver
Bibliographic record
Abstract
The design of a second amplification stage (SAS) for a highly sensitive direct-sampling receiver for the Square Kilometre Array (SKA) radio synthesis telescope is discussed. The SAS is intended to follow a Square Kilometre Array low-noise amplifier (SKA-LNA), which is being designed by others and is not a subject of this study, to obtain the high gain required from the SKA receiver. Due to the SKA ultra-low noise-temperature requirements, the SAS noise must be minimized, even though it is preceded by an SKA-LNA. The first two stages of the SAS consist of an inductorless partially noise-canceling resistive-feedback amplifier and a differential gain stage that achieve both low noise figures (NFs) and convert the single-ended input signal to a differential output. Following this, an additional gain stage is cascaded to increase the SAS gain. Over the midband SKA frequency range of 0.7-1.4 GHz, a 65-nm CMOS SAS achieves an S21>; 34 dB, voltage gain >;36 dB, and sub-1-dB NFs (~75-K noise temperature), P1dB of >; -52 dBm, input third-order intercept point (IP3) of >;-43 dBm and input second-order intercept point (IP2) of >;-34 dBm, while consuming 96.8 mW of dc power. While the proposed SAS is not required to be input power matched, a method for matching with minimum effect on NF and gain is also presented and experimentally verified. The power match SAS achieves an S21>;26dB, voltage gain >;35 dB, and sub-1.6-dB NFs (~130-K noise temperature), input P1dB of >;-52 dBm, input IP3 of >;-44 dBm, and input IP2 of >;-34 dBm, while consuming 58.9 mW of dc power.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".