Scanless Spectral Imaging of Terahertz Vortex Beams Generated by High‐Resolution 3D‐Printed Spiral Phase Plates
Bibliographic record
Abstract
Terahertz technology has experienced significant advances in the past years, leading to new applications in the fields of spectroscopy, imaging, and communications. This progress requires the development of dedicated optics to effectively direct, control and manipulate terahertz radiation. In this regard, 3D printing technologies have shown great potential, offering fast prototyping, high design flexibility, and good reproducibility. While traditional 3D printing techniques allow for the preparation of terahertz optical components operating at relatively low frequencies (<0.4 THz) due to their limited resolution, two-photon polymerization lithography (TPL) exhibits high detail resolution and low surface roughness and can thus potentially enable the fabrication of high-frequency terahertz devices. Here, as a proof of principle, spiral phase plates operating at 1 THz are designed and fabricated by means of TPL. Moreover, these samples are characterized via a rapid and scanless terahertz imaging technique customized to obtain a coherent hyperspectral analysis of the generated vortex beams at varying distances along propagation. Numerical simulations are also conducted for comparison with experiments, revealing a good agreement. Current limitations of the technique are found to be mainly related with terahertz loss in TPL polymers, and possible solutions are discussed.
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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".