A 65 nm CMOS broadband self‐calibrated power detector for the square kilometre array radio telescope
Bibliographic record
Abstract
In this study, a 65 nm complementary metal oxide semiconductor (CMOS) broadband self‐calibrated high‐sensitivity power detector for use in the Square Kilometre Array (SKA), the next‐generation high‐sensitivity radio telescope, is presented. The power detector calibration is performed by adjusting voltages at the bulk terminals of the input transistors to compensate for mismatches in the output voltages because of process, voltage and temperature variations. Measurements show that the power detector, preceded by an input power‐match circuit with 6 dB gain, has an input signal range from −48 to −11 dBm over which a 0.95 dB maximum error in the detected power is observed when the calibration rate is 20 kHz. The proposed broadband power detector has a 3 dB upper band edge of 1.8 GHz, which adequately covers the midband SKA frequency range from 0.7 to 1.4 GHz. The settling time and the calibration time are both <5 μs. The circuit consumes 1.2 mW from a 1.2 V power supply and the input‐match circuit consumes another 5.8 mW. The presented power detector achieves the best combination of the detection range and sensitivity of previously published circuits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".