A Planar Electronically Steerable Patch Array Using Tunable PRI/NRI Phase Shifters
Bibliographic record
Abstract
This paper presents a planar electronically steerable series-fed patch array for 2.4-GHz industrial, scientific, and medical band applications. The proposed steerable array uses 0deg tunable positive/negative-refractive-index (PRI/NRI) phase shifters to center its radiation about the broadside direction and allow scanning in both directions off the broadside. Using the PRI/NRI phase shifters also minimizes the squinting of the main beam across the operating bandwidth. The tunable PRI/NRI phase shifters employ 0.13-mum CMOS tunable active inductors, as well as varactors in order to extend their phase tuning range and maintain a low return loss across the entire phase tuning range. The feed network of the proposed array uses lambda/4 impedance transformers. This allows using identical interstage phase shifters, which share the same control voltages to tune all stages. Furthermore, using the impedance transformers in combination with the CMOS-based constant-impedance PRI/NRI phase shifters guarantees a low return loss for the antenna array across its entire scan angle range. The antenna array was fabricated, and is capable of continuously steering its main beam from -27deg to +22deg off the broadside direction with a gain of 8.4 dBi at 2.4 GHz. This is achieved by changing the varactors' control voltage from 3.5 to 15 V. Across the entire scan angle range, the array return loss is less than -10 dB across a bandwidth of 70 MHz, and the relative sidelobe level is always less than -10 dB. Furthermore, the proposed design achieves very low beam squinting of 1.3deg/100 MHz at broadside and a 1-dB compression point of 4.5 dBm.
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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 | 0.001 | 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".