A Single-Chip Liquid Crystal Variable Capacitor Using a Microfabrication Process for Tunable RF and Microwave Applications
Bibliographic record
Abstract
This article presents the implementation of a single-chip silicon-micromachined variable capacitor employing nematic liquid crystal (LC). The LC substance is confined within a slender micromachined enclosure, enabling electronic tuning of its dielectric characteristics. The compact dimensions of these chip capacitors facilitate their utilization across an extensive range of RF tunable/reconfigurable applications. This article thoroughly explains the fabrication procedure and includes comprehensive discussions on simulation as well as measurement results of the chip-based LC variable capacitors. Three chip capacitor configurations are demonstrated: 1) single port shunt capacitor; 2) two-port series capacitor with an integrated bias line; and 3) two-port series capacitor. The shunt capacitor displays an 18% variation in capacitance, while the quality factor ranges from 44 to 123. The measurements of the capacitor with the integrated bias line reveal a 21% tuning range accompanied by a quality factor ranging from 22 to 45 at 1.25 GHz. On the other hand, the two-port series capacitor exhibits a 23% change in capacitance values while demonstrating a quality factor that varies between 30 and 105 at 1.25 GHz. The chip capacitors discussed in this work are manufactured utilizing an in-house multilayer microfabrication process. An ion beam irradiation (IBI) process is developed for the treatment of the prealignment layer of highly miniature capacitors.
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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 | 0.000 |
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".