Suppression-Enhanced Edge-Coupling Capacitors for Microwave and Millimeter-Wave LTCC On-Chip Single-Ended and Differential Filters
Bibliographic record
Abstract
In this article, a novel suppression-enhanced edge-coupling capacitor (SEECC) is proposed for the implementation of on-chip microwave and millimeter-wave (mm-wave) filters in the low-temperature co-fired ceramic (LTCC) platform. The parasitic inductance of the quasilumped capacitor (QLC) is effectively reduced using only vertical via holes, thereby extending the spurious-free stopband. Next, SEECC-based single-ended drive resonators (SEDRs) and differential drive resonators (DDRs) are proposed to construct single-ended bandpass filters (SEBPFs) and differential bandpass filters (DBPFs) using a hybrid coupling technique (HCT). For verification, two SEBPFs operating at 8.4 and 27.3 GHz are designed, with their 20-dB stopbands up to 34.5 and 67 GHz. The 8.4- and 27.3-GHz filters achieve low insertion losses (ILs) of 0.7 and 1.2 dB. Furthermore, a 22.5-GHz DBPF with a differential-mode (DM) stopband up to 67 GHz is realized. The quasifull common-mode (CM) stopband is obtained from 0 to 67 GHz. The roll-off factor (ROF) is up to 0.4. This design concept not only contributes a universal method for harmonic suppression, low radiation effects, and quasifull (CM) rejection but also achieves a high-selectivity on-chip DBPF.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".