Design of a microwave channelized active filter for MMIC
Bibliographic record
Abstract
The emergence of new wireless communication systems is always increasing the need for smaller, lighter and cheaper components while technical issues are harder to address as higher frequencies are used. We present the design of a fully integrated microwave bandpass filter, using only lumped components and HEMTs, to fulfill the input requirements of a LMDS client. The channelized filter approach has been used, since it has been demonstrated that this approach can achieve high selectivity in high frequency bands. A three branches design is used. All branches must be optimized so that their individual responses are added in the bandpass while they interfere outside to result in a very selective behavior. Each branch comprises two identical amplifiers and a third order Butterworth filter properly optimized. Final design simulations show that the filter is very selective with a 400 MHz bandpass bandwidth centered at 28.1 GHz. The bandpass gain is slightly over 4 dB and the rejection over 80 dB. The resulting circuit would cover an area of about 10 mm2. Further study shows that the circuit is somewhat sensitive to component tolerances. However, the sensitivity is associated with the lumped filters components used in each branch and not with the amplifiers' characteristics. Consequently, manufacturing yield should not be substantially less than the yield of the HEMT process itself
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".