Bibliographic record
Abstract
Current tunable devices such as filters, impedance matching networks and oscillators have problems that degrade their performance at high microwave frequencies. Tuning ratios and quality factors are the major problems associated with semiconductor based tuning components. This thesis presents the design, fabrication and testing of two novel RF MEMS tunable capacitors. The first tunable capacitor is designed using electrostatic repulsive-force actuators which produce an upward movement of the moving plate of a tunable capacitor. The repulsive-force actuator is free of pull-in effect and capable of reaching large displacement. Gap increasing tunable capacitors with areas of 162μm×220μm and 300μm×302μm are developed using electrostatic repulsive-force actuators. The capacitances are calculated using simulations and maximum tuning ratios of 438.5% and 230% are obtained for a parallel and inclined plate designs, respectively, with capacitance-voltage linearity of 96.28% and 95.14%, respectively, in the presence of RF voltage. The second tunable capacitor is developed using residual stress gradient based vertical comb-drive actuator. Conventional vertical comb-drive actuators need two vertical comb fingers, i.e., one for the fixed and one for the moving comb. MetalMUMPs process provides a 20μm thick nickel layer which is subject to residual stress gradient along its thickness. Using the residual stress gradient two curve-up beams are devised to bend out of plane and upward. A moving plate is connected between the middles of the curve-up beams through supporting springs and is raised above the substrate. The moving fingers are connected to opposite sides of the moving plate. The fixed comb-drive fingers are anchored to the substrate. When a voltage is applied, the moving fingers move down towards the fixed fingers. As a result, the capacitance between the moving fingers and the fixed fingers change. Prototypes are fabricated to verify the working principles of this novel actuator using the MetalMUMPs process. Tunable capacitors based on this actuator are experimentally analyzed. Quality factors of 106.9-162.7 at 0.8GHz and 42.4-51.9 at 1.24GHz are obtained over actuation voltage of 0-100V. An optimal design of the tunable capacitors achieved a tuning ratio of 194.4% at 162.5V with linearity of 97.84%
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".