Very Small Footprint 60 GHz Stacked Yagi Antenna Array
Bibliographic record
Abstract
Millimeter wave applications such as short-range high-speed wireless links require modular, compact-size and high-directivity antennas. In this paper, high-gain compact stacked multilayered Yagi designs are proposed and demonstrated in the V-band. This novel design shows for the first time an antenna array of Yagi elements in millimeter wave stacked structure. To demonstrate the proposed concepts and design features, a 4 × 4 antenna array is created having excellent gain performance as well as very small footprint. A single element stacked Yagi antenna fed with microstrip is studied in order to obtain the desired performance. An analysis is performed to define the structure limitations. Measured results of the fabricated antenna prototypes are in good agreement with simulated results The measured Yagi antenna attains 11 dBi gain over 4.2% bandwidth with a size of 6.5 × 6.5 × 3.4 mm3. A 4 × 4 array of Yagi antenna using an SIW (Substrate Integrated Waveguide) feeding technique is conceived. Both simulated and measured results match with each other very well. The 4 × 4 array has a size of 28 × 24 × 2.4 mm3, and reaches a measured gain of 18 dBi over 7% bandwidth. An alternate configuration of the array using angled Yagi antenna elements allows a significant improvement of the side lobe level (SLL) with a low impact on the gain performances. The proposed antennas are excellent candidates for integrated low-cost millimeter-wave and even terahertz systems. The small foot print, the antenna design flexibility as well as its easy adaptation to automatic fabrication processes are good assets for making short range portable imaging systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".