Coplanar waveguide structures on micromachined glass substrates
Bibliographic record
Abstract
Micromachining is currently being studied to help reduce the packaging and integration costs of multi-chip modules (MCMs) in radio frequency (RF) systems. Such systems require the integration of radiating elements, feed networks, micro-electromechanical systems (MEMS), and monolithic microwave integrated circuits (MMICs). Each subsystem has its own requirements in terms of material properties. Antennas radiate best when located on low dielectric constant materials, while higher dielectric constant materials permit smaller size for feed networks. In both cases low loss is a practical concern. Ideally one material could be used which would satisfy all the requirements. In this work a sequence of processes to create thick copper coplanar waveguide (CPW) structures on low-cost aluminosilicate glass substrates is proposed and demonstrated. Conductor loss as a function of geometry and conductor thickness will be discussed. Results will be presented that indicate the effective dielectric constant of a single substrate material can be varied by micromachining an air/dielectric lattice structure beneath perforated conductors. This micromachined perforated CPW (MCPW) structure can effectively provide multiple dielectrics with a single material. The micromachined structures are characterized against a regular finite ground CPW and compared against numerical predictions.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".