Miniaturized GNSS Antenna Feeding Networks Using Multi-Layer LTCC Technology
Bibliographic record
Abstract
Circularly Polarized (CP) antennas are crucial elements in wireless communications, and for geospatial applications such as Global Navigational Satellite Systems (GNSS), their Right-Hand Circular Polarization (RHCP) is an essential property. External feeding networks are responsible for the CP of many antennas, whose function is to excite two orthogonal modes (specific to that antenna) in quadrature phase – but the method at which this is achieved ultimately depends on the type of antenna. Two examples of specific feeding networks are: a) 4-port antennas (i.e. Dielectric Resonator Antennas (DRAs) and Printed Quadrifilar Helical Antennas (PQHAs)) that require an incremental 90° phase delay fed to each port, and b) intrinsically Linearly Polarized (LP) antennas arrays that are fed in relative quadrature phase to their single ports (i.e. 2x2 array of sequentially rotated microstrip patch antennas). This thesis explores the use of Low Temperature Co-fired Ceramics (LTCC) to highly miniaturize the aforementioned antenna feeding networks into System-on-Package (SoP) solutions with Surface-Mount Technology (SMT) and full 3-D shield features for GNSS frequencies. LTCC technology offers the advantages of reimagining common lumped-element circuits based on passive Surface-Mount Devices (SMDs) as extremely compact multi-layer structures. Quadrature phase and signal division respective to each feeding network is realized as the combination of various multi-layer lumped-element power splitters and 90°/180° hybrid couplers. In addition, stand-alone chips or dies of each circuit are presented, complementing this thesis as commercially viable products suitable for wideband or dual-band applications over the GNSS spectrum. All circuits were fabricated using 14-layers of FerroA6M LTCC substrate with an 𝜀𝑟= 5.7, tan𝛿=0.001, (from 1-2GHz) and a homogenous layer thickness of 90μm. Simulations were conducted with ANSYS’ High Frequency Structure Simulator (HFSS®) and Keysight’s Advanced Design System (ADS®) which are in good agreement with measurements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".