Self‐assembled triangular waveguide slot array for system‐on‐chip applications
Bibliographic record
Abstract
New microelectronic fabrication techniques enable the creation of high‐performance on‐chip antennas by using new or modified antenna designs. A system's performance is normally improved by a high antenna gain which requires an electrically large aperture typically supporting an array. Consequently, array design is of particular interest for high‐performance on‐chip systems. The feed for an array is a challenge because it must be low loss while extending over the large aperture. A solution is the waveguide slot array which integrates antenna elements with a low‐loss feeding structure. This study presents a new slot array structure which is motivated by advanced microelectronic fabrication. It comprises a triangular waveguide as the array feed, and slots for the elements. This allows fabrication by a self‐assembly technique which lithographically controls the shape of a three‐dimensional structure. This process is complementary metal–oxide–semiconductor compatible, enabling radio and radar system‐on‐chip solutions. For production, the low mass of the structure makes it inherently robust, although hermetic packaging with a radio window akin to UV‐erasable eproms would be required (not included here). The design concept is demonstrated with a physical prototype operating at a low frequency. The fabrication concept is demonstrated by a physical on‐chip prototype, and its performance is assessed by simulation.
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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 | 0.000 |
| 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".