A wideband combined LNA‐filter in 22 nm FDSOI technology for satellite phased arrays
Bibliographic record
Abstract
Abstract This article introduces a new, low cost and complexity, wideband Ku/K‐band combined low‐noise amplifier (LNA) Filter designed for large satellite phased array systems in advanced 22 nm Fully Depleted Silicon on Insulator Technology. The LNA achieves the required 20 dB gain through a two‐stage configuration using the cascode configuration. To effectively reject the transmitter (TX) signal, the LNA integrates 4 distributed parallel LC notch filters. Measured IP1dB at 29 GHz (TX band) demonstrates a 25 dB increase compared to IP1dB at 19 GHz (RX Band), with a remarkable 15 dB enhancement over the LNA without the filter. The 1‐dB gain bandwidth spans from 16 to 21 GHz, representing a 27% fractional bandwidth. Over the entire bandwidth, input/output return losses exceed 10 dB, maintaining a low noise figure (NF) of less than 3.5 dB. Inclusion of two filters at the input and middle of the first stage results in approximately 0.6 dB of increase in NF when compared to the LNA without the filter.
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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) | 0.000 | 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 | 0.001 |
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".