Nonlinear Photonic Waveguides: A Versatile Platform for Terahertz Radiation Generation (a Review)
Bibliographic record
Abstract
Abstract Continued development in the areas of communication, security, medicine, and safety is calling for terahertz‐wave technologies to evolve beyond laboratory research and into the realm of real‐world applications. Due to the compatibility of waveguides with on‐chip fabrication techniques and their ability to realize a small footprint, such structures provide a unique opportunity to achieve terahertz radiation generation in an on‐chip arrangement, ideal for practical applications requiring terahertz electric fields. This review considers waveguiding arrangements that produce terahertz radiation using the nonlinear frequency‐conversion techniques of optical rectification and difference frequency generation. The investigated terahertz radiation waveguiding sources are categorized into three different classes: those that confine and guide both the excitation electric fields and the generated terahertz radiation, those that confine and guide the excitation electric fields but not the generated terahertz radiation, and those that confine and guide the generated terahertz radiation but not the excitation electric fields. Various types of waveguides are surveyed from each category, including dielectric waveguides, metallic waveguides, photonic crystal waveguides, poled waveguides, and multiple waveguides embedded within one another. This review provides a detailed overview of state‐of‐the‐art terahertz radiation waveguide sources, with the intent of aiding in the continuing development of such sources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".