Monolithically Integrated RF MEMS-Based Variable Attenuator for Millimeter-Wave Applications
Bibliographic record
Abstract
This paper reports a millimeter-wave radio frequency (RF) microelectromechanical systems (MEMS)-based variable attenuator implemented by monolithically integrating CPW-based hybrid couplers with lateral MEMS varactors on a silicon-on-insulator (SOI) substrate. The MEMS varactor features a Chevron-type electrothermal actuator that controls the lateral movement of a thick plate allowing precise change of capacitive loading on a CPW line leading to a change in isolation between input and output. The proposed variable attenuator is successfully fabricated on an SOI substrate with a device footprint of 3.8 mm × 3.1 mm. The fabrication process provides flexibility to extend this module and implement more complex RF signal conditioning functions, thus making it more appealing to realize a wide range of reconfigurable RF devices. The measured RF performance shows that the device exhibits attenuation levels (S21) ranging from 10 to 25 dB, at the center frequency of 60 GHz with a bandwidth of 4 GHz and a return loss of better than 20 dB. The device involves laterally moving 20-μm-thick structures, hence offers reliable operation and eliminates MEMS problems like stiction, dielectric charging, and microwelding observed in surface micromachined thin membranes.
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) | 0.000 | 0.000 |
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".