Monolithically Integrated Reconfigurable RF MEMS Based Impedance Tuner on SOI Substrate
Bibliographic record
Abstract
This paper presents the design and implementation of a MEMS-based impedance tuner realized on a Silicon-on-Insulator (SOI) substrate. Contactless lateral MEMS varactors were realized using laterally moving capacitive thick plates whose motion was precisely controlled using Chevron actuators. The voltage required for the maximum displacement is under 12 V. These varactors are monolithically integrated with CPW lines using a single mask fabrication process on SOI substrate. The implemented MEMS capacitive varactors exhibit a capacitance range of 0.19 pf to 0.8 pf. The improvement of the Smith chart coverage is achieved by proper choice of the electrical lengths of the CPW lines and precise control of the lateral motion of the capacitive plates. The measured results demonstrate a good impedance matching coverage with an insertion loss of 2.9 dB. Details of the SOI-based fabrication process are presented along with discussions on techniques to improve the insertion loss of the device. The proposed design does not suffer from the dielectric charging, micro-welding and stiction problems associated with RF MEMS devices realized using surface micromachining processes. In addition, the device promises to be useful in high power applications, since it is constructed from lateral thick structures.
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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".