High-Performance, Compact Quasi-Elliptic Band Pass Filters for V-Band High Data Rate Radios
Bibliographic record
Abstract
This paper presents a comparison of the design and implementation of V-band quasi-elliptic band pass filters suitable for system-on-package integration at millimetre-wave frequencies. Filters were designed and manufactured at low-temperature co-fired ceramic (LTCC) and alumina substrates. Filter circuits include the input/output coplanar waveguide to microstrip transitions as well as the microwave pads, used to facilitate measurement. Three four-pole filters incorporating half-wavelength resonators were implemented, two planar (on LTCC and alumina substrates) and one vertically stacked (on LTCC). In addition a six-pole filter was also implemented in alumina. The four-pole LTCC based filters have a measured insertion loss (IL) as low as 3.4 dB, fractional bandwidth (FBW(%) = BW-3 dB/Center Frequency) of 4.8% and return loss better than 10 dB. Total filter size is less than 1.1 × 0.74 mm. The alumina-based four-pole/six-pole filters exhibit a measured IL of 2.8/3.1 dB, FBW of 8.3%/12.6%, respectively. Both alumina filters exhibit a return loss better than 10 dB and with a corresponding filter layout footprint of less than 1.01 × 1.34 mm. A new figure-of-merit (HPFOM) is proposed and results from the filters proposed here clearly show they offer the best trade-off between performance and area (higher HPFOM).
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".