Novel High-$Q$ MEMS Curled-Plate Variable Capacitors Fabricated in 0.35-$\mu{\hbox {m}}$ CMOS Technology
Bibliographic record
Abstract
Two microelectromechanical systems (MEMS) curled-plate variable capacitors, built in 0.35-mum CMOS technology, are presented. The plates of the presented capacitors are intentionally curled upward to control the tuning performance. A newly developed maskless post-processing technique that is appropriate for MEMS/CMOS circuits is also presented. This technique consists of dry-and wet-etching steps and is developed to implement the proposed MEMS variable capacitors in CMOS technology. The capacitors are simulated mechanically by using the finite-element method in ANSYS, and the results are compared with the measured results. Two novel structures are presented. The first capacitor is a tri-state structure that exhibits a measured tuning range of 460% at 1 GHz with a flat capacitance response that is superior to that of conventional digital capacitors. The proposed capacitor is simulated in Ansoft's high frequency structure simulator (HFSS) and the capacitance extracted is compared with the measured capacitance over a frequency range of 1-5 GHz. The second capacitor is an analog continuous structure that demonstrates a measured continuous tuning range of 115% at 1 GHz with no pull-in. The measured quality factor is better than 300 at 1.5 GHz. The proposed curled-plate capacitors have a small area and can be realized to build a system-on-chip.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".