Planar Porous Components for Low‐Loss Terahertz Optics
Bibliographic record
Abstract
Abstract There is a strong interest in using the terahertz (THz) frequency band for applications in sensing, imaging, and wireless communications. To enable many of these applications, compact low‐loss components for beamforming are required. Typically, such components are made using solid dielectric elements with spatially variable thickness, for example, planoconvex lenses or spiral phase plates. However, as losses in dielectrics typically greatly increase with THz frequency, so do the losses of the solid components. This work demonstrates that when introducing low‐refractive index, low‐loss subwavelength inclusions (air holes) into a solid material matrix, the loss of porous components can be greatly reduced compared to the loss of solid components with otherwise identical optical properties, thus opening a way to create efficient optical components even with nominally high‐loss materials. Additionally, porous optical components can be created completely flat as spatially dependent optical path difference is achieved by varying the local porosity rather than the component thickness. This offers additional advantages for free‐space alignment and integration of such components into optical systems. As an example, the design, fabrication, and experimental characterization of planar lenses and planar orbital angular momentum phase plates are carried out. It is then demonstrated how these porous components outperform their all‐solid counterparts.
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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 | 0.000 |
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".