On the fabrication of thin-film artificial metal grid resonator antenna arrays using deep x-ray Lithography
Bibliographic record
Abstract
Abstract In this paper, a unique microfabrication approach for designing high permittivity, high gain and wideband thin-film artificial metal grid dielectric resonator antenna (GDRA) arrays for mm-wave applications is presented. Fabrication of the GDRAs with a strip template frame is based on deep-x-ray lithography (DXRL) using a 500 µ m thick polymethylmethacrylate (PMMA) layer as the photoresist. The simple low-cost fabricated DXRL mask for exposure consisted of gold absorbers patterned by direct-write ultraviolet (UV) laser lithography on a carbon substrate. To avoid the trapping of gas bubbles in the dense, high aspect ratio (HAR) deep grid structures, a novel development approach was introduced utilizing in-vacuum, room temperature dip development to facilitate the collapse of forming gas bubbles. The developed voids were subsequently electroplated to a nickel thickness of 300 µ m to obtain high permittivity artificial dielectrics. The paper describes the entire fabrication sequence. Also, the x-ray mask and DXRL structures are examined for dimensional accuracy and structural quality. This strip template frame approach has superior performance in terms of impedance bandwidth, gain and radiation efficiency as compared to the previously published solid template frame approach. For the purpose of the demonstration, a four-element GDRA array sample was fabricated and tested. A wide measured impedance bandwidth of 5 GHz from 58 GHz to 63 GHz and measured broadside radiation pattern with a peak gain of 10.9 dBi was achieved.
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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) | 0.001 | 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".